EMR: No Shortage in Standards

As we transition to a full EMR system, one of the largest current challenges to our health care model is the need to share information at different sites: hospitals, clinics, nursing homes, tertiary care centers, etc. To help facilitate this interoperability between health care sites, standards are needed to guide us towards this end.

Even to begin to set such standards and/or goals towards interoperability, there first must first be a focus to aggregate patient data towards a unified standard. In setting the stage for this ‘consensus’ standard, all stake holders who represent some specific arm of medicine must come together and define the standards that allow them to work together across sites and distances.

Many of these respective groups that create standards have a tight relationship; some have absolutely no connection whatsoever. Thus, it’s easy to see this creates an inherent redundancy in standards in some areas of gap in others. Such redundancy and scattered coverage in standards have created the need for harmonization.

 

The graphic above represents the 43 major international health care standard developing organizations (SDO's) that are working together to create seamless interoperability. Many of these organizations have a need for assistance, and are free to join and have openings for volunteer opportunities. If you are interested in breaking into HealthIT, anyone of these organizations could mark the start of your transition towards this career.

As I continue to navigate through my HITECH program, I will continue to highlight many of these organizations and give you a bird's eye view of their role in the EMR interoperability equation. If you'd like to learn more about EMR transition and the mountain we are facing, click here.

About the author:

Mehdi Rais is a physician, medical lecturer, medical writer, and self-proclaimed “tech nerd.” When Dr. Rais isn’t honing in on his trades, he spends his time scouring publications and the web for the latest trends in technology in the medical field, new applications in Health Information Technology, and emerging legislative & regulatory changes in medicine.

Dr. Rais' interests are greatly focused in the realm of mobile computing and the use of cell phone technologies in the clinical setting. He received his Medical Doctorate from St. Christopher’s College of Medicine after spending his undergraduate years at the University of Texas at Dallas.

Dr. Rais can be reached on LinkedIn or followed at his blog here. Look for him on Twitter @DrMedBlog.

EMR Transition: It's all in the Words....

Seamless care through EMR requires establishing interoperability between different systems (i.e. organizations, hospitals, clinics, networks, etc.)  The starting point for achieving this interoperability is producing a controlled vocabulary to input within a database.  Naturally, over time development organizations have created such data sets in vocabulary for the specific aim to explain a specific arm of medicine.  Gradually, these organizations have broadened their purpose to encompass all medical terminology.  With this in mind, it is important to note that none of these initial data sets were ever intended to cover all of medicine and thus the reason we have overlap in terminologies, descriptions, etc.

If one were to simply take a look at a single term/data set and the variance in definitions of this term, it would be easy to see how the transition to EMR could effectively come to a grinding halt in its attempts to achieve complete interoperability.  A recent study examined the term myocardial infarction (MI). We all have our own conceptual model of MI and a respective qualitative/quantitative description to fit this model. The study I am referring to found 60 different scientific definitions of myocardial infarction.  Take this example one step further, and imagine the EMR interoperability nightmare if the records of one MI patient had to seek care or advice from a different institution. The flow of that data set from one institution to the next would immediately be severed simply because of the differing definitions and descriptors given. Now multiply this problem by every diagnosable disease.  The thought of standardization of language and disease description becomes a staggering one, and here lies the mountain of work we must climb to achieve seamless EMR interoperability.

To put this into further perspective, the following table elaborates the choices in terminology for specific recordable data sets and the respective organizations that are attempting to develop a controlled language.  While there may be overlap in some of the language in one or two of the systems, there is by no means a consensus language. 

 '+' represents the amount of use of a controlled terminology set within the U.S.; '$' represents a respective cost to the end user.

Having said this, clinicians cannot sit back and simply think that EMR transition will be done by IT people alone.  Definitions and vocabulary are the back bone of this system. Such terminology can only be given by clinicians.  Currently the Office of the National Coordinator estimates 50,000 people will be needed to assist in this effort over the next 4-5 years to reach our goal of 100% transition to EMR.  

I for one had no idea of the scale of the interoperability problems without having started the HITECH Workforce Program only a few weeks back.  I look forward to presenting many more of the big issues that we are facing during this EMR transition in the coming months.  

About the author:

Mehdi Rais is a physician, medical lecturer, medical writer, and self-proclaimed “tech nerd.” When Dr. Rais isn’t honing in on his trades, he spends his time scouring publications and the web for the latest trends in technology in the medical field, new applications in Health Information Technology, and emerging legislative & regulatory changes in medicine. 

Dr. Rais' interests are greatly focused in the realm of mobile computing and the use of cell phone technologies in the clinical setting.  He received his Medical Doctorate from St. Christopher’s College of Medicine after spending his undergraduate years at the University of Texas at Dallas.  

Dr. Rais can be reached on LinkedIn or followed at his blog here. Look for him on Twitter @DrMedBlog.

Blood Pressure Showdown: Withings vs iHealth

You've read a few reviews on iOs enabled blood pressure cuffs breaking into the market (here, here, and here.)  I was interested in taking a closer look at the devices available and their respective hardware and user interface.  In the following chart, you'll find some pretty big differences between the offerings of the two main manufacturer's (iHealth and Withings) in terms of power, user interface, ability to share, analytical capabilities, and more....

SPEC COMPARISON

With Specs in mind, let's take a quick glimpse at the hardware package and UI.

WITHINGS BLOOD PRESSURE CUFF & APP: 

A look at the size and portability of Withing's product.

UI while acquiring blood pressure

Blood Pressure results display

Graphical results of blood pressure readings

iHealth's BLOOD PRESSURE CUFF, BASE & APP:

Despite the base station, iHealth's offering is still highly portable.

UI while acquiring blood pressure. 

Blood pressure results

Graphical interpretation of blood pressure data. 

Determining a champion in this showdown is really a matter of personal preference.  If you or your patients need a product that offers dynamic avenues to share, Withings is your product.  If you need a more robust and appealing UI and user experience, iHealth gets the nod.  At the end of the day, you can't lose with either product.  

Which one will you be advising your patients to grab?