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Did Your ‘Check Engine’ Light Come On?

Twenty years ago, an automotive mechanic would listen to your car or feel how it shifts to know that your starter or transmission was going out and needed to be replaced. Today, cars are controlled by computers, and to diagnose a problem, mechanics connect their computer via a cable to an input/output port on the car.  They use a special program on their computer to access various diagnostic codes stored on the car's computer, which the industry calls an electronic control module (ECM). 

Gone is the intuitiveness of the mechanic’s hearing and feeling, replaced by the computer program used by the service technician who analyzes the codes and offer suggestions on how to repair the car.  The huge amount of data from connected sensors is gathered and analyzed and instantly provides information to service technicians enabling predictive and even real time car dashboard indicators to keep you safe, on the road, and preventing breakdowns before they occur.  At the Consumer Electronics Show in January 2015, General Motors announced a new suite of tools that uses cloud technology to let owners know which parts of their car are likely to need service, and provides feedback on driving habits for owners to earn insurance discounts.

Are there analogies to our healthcare system of today? YES! 

Technology has become an essential part of our everyday lives providing personal guidance and valuable information.  If a computer could accurately determine service to a car, can you imagine waking up to a "YOUR BODY NEEDS SERVICE!" notification this morning? Or perhaps, an alert, “Your colon just can't go more than 4 days without needing the colonoscopy, you have hit 400 days.

Maybe you have heard of the experienced nurse or physician who had a “sixth sense” or intuition.  Several studies have been conducted on intuition in clinical settings, but comprehension of this concept is unclear.  Much like the mechanic who listened to the sound of the engine, clinicians could detect early deterioration of a patient, even though the signs and symptoms did not reflect such. 

As it is with the car industry, with the science of healthcare rapidly changing and patient complexity growing (i.e. multiple comorbidities, polypharmacy, etc.), it is nearly impossible for a single clinician to put all the pieces together to accurately predict and treat all on their own. Complexity theory suggests that keys to understanding the system are contained in patterns of relationships and interactions among the system’s agents.  These are rarely linear in behavior. 

We won’t know what the smart decision will be until we see the uniqueness of each scenario as it plays out.  We no longer have a single place to start “diabetic patient.” It is a diabetic patient who presents in the Emergency Department with a broken hip, who has not been taking their medication for heart failure, and also lives alone.  Every person needs a precision health response.

The massive volume of health care data that is generated from the healthcare ecosystem requires sophisticated information technologies for storage, aggregation, and analyses.  Additionally, the complexities of data generated from unconnected, disparate systems, sometimes from different parts of the globe, present challenges. 

Systems must be designed to do real-time data processing to deliver knowledge in a timely way. Getting the right information to the right person at the right time, so that informed decisions can be made, is how care is impacted. And as quickly as data volumes have spiked, so too have the expectations for improved patient outcomes, cost accountability, population needs, and risk management. This requires us to find meaning and connections in real-time, and with greater financial efficiency, to deliver better patient care.

It is no longer simply acceptable to store healthcare data, but rather, we must have protocols in place for these data to be integrated, allowing us to derive actionable knowledge through analysis. These demands have created nothing short of a seismic shift for our industry, requiring a transformation in how we think about information in all forms and from all sources.   

The HIMSS C&BI committee is gathering and identifying various non-traditional – outside the four walls of the hospital – data sources, describing its contextual aspects and potential utilization, and creating various use case sceneries to help you best leverage the plethora of available data to meet your strategic and tactical goals.     

  • What non-traditional data sources do you utilize for your work?
  • What are some the challenges you face with data access, integration, and utilization? How have you dealt with these issues?
  • Are there any policy or regulatory barriers you have experienced in your efforts to leverage data in value-based care?
  • What role do you see data playing in the transformation of healthcare?

Join the HIMSS C&BI Community today to learn more about applying data and analytics to improve health, and other critical topics that will help you on your journey to Turn Data to Action.

Keywords: 
big data; data management; predicative analytics; precision medicine; clinical and business intelligence