Healthcare and The Long Tail

My neighbor brought her son to the doctor this summer for a rash that
wouldn’t go away. “The doctor had never seen anything like it,” she
explained.  “In fact, he brought in the other doctors to take a look at
it, and none of them had ever seen it either.”  Now I don’t know about
you, but listening to her reminded me of my worst medical nightmare of
things I don’t ever want to hear from my doctor:  “Excuse me, do you
mind if I bring in some of the residents? We’ve never seen a case like
yours before.”

In a recent THCB post,
author Maggie Mahar writes that “Ambiguity haunts medical care”. She
goes on to quote Dr. Atul Gawand – “Uncertainty is the core predicament
of medicine . . . the thing that makes being a patient so wrenching,
being a doctor so difficult and being part of a society that pays the
bills so wrenching."

 

It’s important to note that for a great
many cases, ambiguity is not really an issue.  This is because the
distribution of medical ailments follows a curve very similar to Chris
Anderson’s “Long Tail”,
with a great many common “blockbuster” ailments stacked up high on the
left-hand side of the curve. For those not familiar with the Long Tail,
Anderson describes how Amazon, Netflix, and other online retailers sell
lots of the usual blockbusters, but actually derive more total volume
from 100s of thousands of niche products.  In healthcare, it is the
left side of this distribution curve which inspires (for better or
worse) Wal-Mart, Target, and others to offer “Doc In A Box” services –
Allergies, Bladder Infections, Bronchitis, Ear Infections, Pink Eye,
Sinus Infections, and a full battery of vaccines – all served up for a
fixed price while you wait.

 

On the right hand end of the
curve though, the NIH Office of Rare Disease classifies over 6,000
conditions, each afflicting fewer than 200,000 Americans.  Along this
part of the curve, things do indeed get very ambiguous in a hurry –
both for patients and physicians. Specialization is a response to this
range of ailments (“nichefication” in Anderson’s terms), and brings
physicians repeated cases of a particular nature – giving them the
confidence that they can routinely diagnose and treat a high percentage
of these patients. However, even within a particular specialty area,
cases will naturally follow a distribution curve from typical to
atypical. Unto themselves – atypical cases are just that – one of a
kind aberrations that force physicians to go outside their typical
“comfort zone” of diagnosis and treatment.  For each individual
physician, these atypical cases feel like the exception rather than the
rule. What the Long Tail suggests though, is that taken in their
entirety, these rare cases actually compromise a large percentage of
all medical cases. In fact, over 25 million Americans suffer from a
“rare” condition.

 

This is problematic, because in general,
physicians – and the healthcare system as a whole – are not well
prepared for dealing with the many and inevitable rare cases. In fact, statistics show
that the median time to diagnosis of a rare condition is six months,
and the average is almost three years! When faced with an atypical
case, most physicians will begin to consult the literature, and/or
confer with their colleagues. Ironically, it is at this moment that the
Long Tail shows up again in a quite surprising and often detrimental
fashion. This is because recent studies in social and information
analysis reveal that our network of professional contacts and
information sources follows the same type of distribution curve. In
other words – we all generally tend to connect with the same 15 or 20
trusted colleagues on a regular basis, and we all gather our
information from a limited stream of trusted sources. Beyond this
trusted core lies an entire world of other people and sources we rarely
connect with, if at all – our own social and information “long tail”.
Generally, using a network of trusted sources (while tuning out most
everything else) is actually very efficient at handling a majority of
our day to day needs. For the atypical situation though, just when we
really need to break out of our habitual way of doing things – our
trusted sources generally don’t deliver. They’ve all been drinking from
the same information punch bowl.

 

So, we have to head out to
the right-hand side of the curve – and begin finding and evaluating
people and sources we don’t really know. For a student or researcher,
this type of research can become a time consuming, challenging, but
often rewarding journey. But, for a patient and physician confronted
with a puzzling and life threatening illness, the stakes are much
higher and time is at a premium. In today’s system, the physician often
must address this dilemma by referring the patient to some other
specialist – with the hope that maybe they will have the knowledge or
connections to form a proper diagnosis and treatment protocol in a
timely manner. The patient of course, must continue to move from
specialist to specialist, their rare case still in hand.

 

Not
surprisingly, the Internet has proved both boon and bane in this
situation. Patients and their families are using the Web to dig into
the latest medical research. However, matching a worried patient or
family member against 1,706,532 Google results is usually a
prescription for both confusion and high blood pressure.

 

On
a more encouraging note, patients stuck along the right-side of the
curve with a “niche disease” are using the extraordinary reach of the
Web to discover that they are not so atypical after all. It’s probably
no surprise to THCB readers that patients are banding together around
wikis, chat rooms, blogs and social networks to offer each other
information, empathy, and inspiration. (This is not so different in
network theory terms from when fans of a niche band find each other on
MySpace). Some of these disease state patient networks are sponsored by  pharma marketing, while others are grass-roots efforts, usually led by  a parent or family member related to one of the patients.

 

Physicians
are also turning online in large numbers. Manhattan Research reports
that more than 600,000 physicians are using search engines to find
medical information. Are they searching about how to treat their
day-to-day typical cases? Very unlikely. Chances are, they are
researching an atypical case. However, if two physicians search – for
example, on “phylloides tumors” at Google they each receive the same
list – but have no easy context by which to evaluate the 13,600 +
search results! 

 

Social network software may be one way to
help physicians overcome this “search result overload”, allowing them
to move faster and more confidently outside their circle of trusted
sources and down the long tail, especially when faced with a “rare”
condition (which as we’ve noted – is not such a rare occurrence in the
aggregate). For example, within MyMedwork
, search priorities start with the individual physician’s network of
trusted colleagues and work outward from there. In other words – each
physician gets a totally unique list of search results based on who in
their own extended network is likely to possess useful information.
Because the medical community is so small, it turns out that physicians
are usually quite closely linked to any given article or study, they
are just not aware of the connection. By viewing the social network
connection within their search results – they are then in a position to
more accurately judge the quality of the information – either by
checking with the in-between link (i.e. “Hi Dr. Jones, I notice you’re
connected to Dr. Watson – what do you think of his study on phylloides
tumors?”) – or by contacting the physician directly (i.e. “Hi Dr.
Watson, I see you went to medical school with my colleague Dr. Jones,
do you mind if I ask you a few questions about your study?).

 

The
implications of The Long Tail and social network analysis for
healthcare are just beginning to be explored and understood. It is
already clear though, that as the entire healthcare field continues to
undergo dramatic change, and “atypical” rare disease states become ever
more typical, ambiguity and uncertainty will continue to impact medical
decision making. In this environment, the need to develop richer and
more varied sources of information, and the value of far-reaching,
online social trust networks will become increasingly apparent for
patients and healthcare practitioners alike.