If EHRs continue to rely upon proxy methods to identify patient
populations, they will hinder the acceptance of decision support
and population management tools.
realizing their potential in the digital health age. At a foundational level, practitioners will need uniform problem lists to
provide consistent care across patients. Further, the market
driver behind any standardization initiative will be the need
for problem lists to provide clinical decision support and population management tools with a precise method for identifying diseased patient populations.
The Inadequacies of Proxy Methods
Currently healthcare organizations are using proxy methods
such as medication lists and billing codes to identify their diseased patient populations. Both of these methods, however,
come with a high amount of false positives and negatives. For
instance, the use of medication lists to identify target populations is highly inaccurate because a diseased patient may
not be taking medications or may be receiving treatment elsewhere. The medications thus may not be listed in the EHR.
A nondiseased patient also may be assigned a particular
medication for a very different problem. For instance, if a
healthcare organization attempts to identify all their asthmatic patients by looking at who was prescribed an inhaler,
they are likely including a group of patients who had persistent
coughs, not asthma.
Another common proxy method is the use of diagnoses from
billing codes. This method also contains great uncertainty, as
billing codes often do not precisely reflect clinical information
as it is most relevant to providers. In addition, a single misdi-agnosis on one visit could throw off all further reporting. This
situation can be a particular problem if providers miscode a
diagnosis they are considering that then gets ruled out by later
diagnostic studies. A query for a diseased patient population
based on that initial billing code would then treat this patient
Dave deBronkart, or “E-Patient Dave,” a blogger on the participatory medicine movement, exemplified the reality of
trying to use billing data as clinical information when he attempted to move his health files from a hospital onto Google
Health in early 2009. The result was a smattering of erroneous information because the hospital was transferring billing
data, not clinical data. 1 With these issues, using billing data
to identify a diseased population of patients will come with a
high amount of error.
If EHRs continue to rely upon these proxy methods, they
will hinder the acceptance of decision support and population management tools. For example, if a healthcare center installs a simple decision support tool to remind its practitioners
to give asthmatic patients a flu shot, using one of these proxy
methods will miss a certain segment of the at-risk population.
Further, that decision support tool will create pop-ups on irrelevant cases, annoying the doctors and making them less
likely to pay attention to future reminders.
For decision support tools to be most effective, they must be
extremely accurate—providing the right advice in the right
scenario at the right time. As problems represent a true declaration of a patient’s health, the problem list presents the best
opportunity within a patient record from which to gain the
most precise information for decision support and population
management tools. Yet, the problem list cannot be helpful until the current variation in content and use is addressed.
Addressing the Inaccuracies
Even if a healthcare organization creates policies around the
content of problem lists, achieving uniformity ultimately will
require changing provider behavior. It would be difficult for
practitioners to comply completely with any standardizing
policies, because they are not easy or intuitive requests.
In general, practitioners find that entering standardized
data rather than free text typically takes more effort (e.g., more
clicks) and often does not express the data in a way that best
matches their thought processes. With the problem list, policies to standardize will likely meet the same issues. Practitioners have developed their own problem list styles, and any
policy inherently cannot meet the preference of all practitioners at all times. Therefore, even with good intentions, practitioners’ personal preferences would quickly win over organizational standards in day-to-day practice.
Further, natural human error keeps problem lists from
achieving full accuracy. The most common error today is simply forgetting to add conditions as they are diagnosed. Other
less frequent errors include using incorrect terms to describe a
problem or placing a condition on a patient’s problem list that
Fortunately, the EHR provides new solutions for these very
old problems. To achieve uniformity, the healthcare industry
must create systems and tools that encourage consistency and
completeness in the problem list as well as policies to address
disagreements in utilization.
In a shared medical record system, the issue of who is responsible for maintaining problem lists can be contentious. Many
primary care providers (PCPs) believe that both specialists
and PCPs should add problems to the list. Conversely, many
specialists have suggested that the problem list is solely the
PCP’s responsibility and feel it would be intrusive to add their