Huge volumes of data about patient
health from electronic medical records (EMR), high-throughput molecular data,
insurance claims, the “quantified self” movement, and social media, are rapidly
becoming available. At the same time,
changes in financial incentives such as the utilization of healthcare
exchanges, the creation of ACOs (Accountable Care Organizations) and the growth
of clinical research networks are driving changes in business models that will
have far reaching consequences. Currently
there is a gap between the huge quantities of health data and the
discovery/validation of new approaches to managing the health of patients and
patient populations. There is a tremendous opportunity to develop new statistical
methodologies to pull information out of the data that can be used to improve
the efficiency and effectiveness of healthcare delivery.
Quality improvement by hospital systems. One of
the challenges facing physicians today is deciding which “standard of care” to
follow. In many cases there are numerous
therapeutic options for a patient, all of which are acceptable. Published studies addressing the question are
often sparse, so the decisions are commonly made based on marketing materials
provided by the pharmaceutical companies themselves. In addition, in a “fee-for-service”
environment, there is a perverse financial incentive to choose the most
expensive therapeutic. However, for
hospital systems that accept some of the expense when patients do not respond
well to treatment, such as ACOs, incentives are quite different. Even for traditional “fee-for-service”
institutions, new federal regulations and “meaningful use” criteria are driving
a need to identify and impose optimal care.
How should “optimal care” be
defined? How do health systems utilize patients’ health records to identify treatment
decisions that lead to optimal care? How
can healthcare systems design trials, run from the EMR or other automated data
sources, to prove or disprove the hypotheses generated from retrospective
analyses?
Example.
Our modern healthcare system is
fragmented. This leads to different
providers following different patient outcomes that are tied to the diseases
for which they are responsible. A
cardiologist may prescribe a statin for high cholesterol, but if the patient
taking that statin gets muscle aches they are more likely to go to their family
practitioner; the physician who originally prescribed the medication might
never even find out about the side effects!
If there is institutional motivation, the health record can be used to
track and measure overall health. The
proxy for “overall health” in this scenario may very well be defined as lower
utilization of hospital resources; In a perfect world, patients will agree that
this is a good proxy.
Recruitment for clinical studies. Typical
large trials are run at many different clinical sites in order to ensure the
accrual of enough patients for the study.
In this setting there are often numerous sites that fail to recruit even
a single patient. The availability of
electronic health records creates the opportunity to directly identify the
right patients for a new trial and to target recruitment efforts. This can simultaneously cut down on trial
startup expenses and boost recruitment rates.
Networks of hospital systems are already building this capability
and will have tremendous advantages when competing to run certain types of
clinical studies. However, electronic health records are inherently messy and
incomplete. What is the best way to cut through the noise and identify the right
patients? How early in the course of
disease can patient populations be identified?
Example. PCORnet
is a group of hospital systems who have obtained federal funding to develop an
automated system for pooling and sharing the health data of individual
patients. It is designed to automate
many of the steps involved in conducting clinical trials. If you are a fan of NPR, Diane Rehm devoted a
show to this concept (and PCORnet specifically); you can listen to it here.
A separate, innovative approach to
patient recruitment has been developed through the participation of the patients
themselves. Last year a social networking
web site, Patients Like Me, and a
clinical research organization, inVentive Health, formed
a partnership to advertise recruitment for clinical trials directly to the
patients.
Preventive medicine. A
systematic approach to preventive care will be important for those healthcare
systems who are trying to minimize the future disease burdens of their patient
populations. Historical health data,
high-throughput molecular data, information from social media, data from “quantified
self” devices, and even purchasing data from credit cards can all offer insight
into the current and future health of patients.
Which patients within the health
system are most susceptible to future disease? What sources of data are best able to identify
those patients? What interventions are best able to prevent bad outcomes in the
long term? Integrating all of the relevant sources of information – and
filtering out the irrelevant sources – in order to build disease specific
models of risk will be critical to identifying patients who are appropriate for
preventive medicine efforts.
Example. Consider
the announcement
from CVS that they will stop selling cigarettes in order to better position
themselves as a healthcare delivery company.
As they begin to provide healthcare services they will accrue health
data on their customers which can presumably – barring legal restrictions – be
tied to other purchases. Purchases of candy
bars, shampoo and razors can easily become part of your electronic health
record. If one of the first signs of
dementia is neglect of personal hygene, CVS may be the first to know when
grandma is developing Alzheimer’s disease!
CVS is not alone in this new business model; Walmart, Target and
Walgreens all have clinics in at least a subset of their stores.
Precision medicine. Until
now, clinical research has favored a “one size fits all” approach to the
development of novel therapeutics. This
is driven by a desire to maximize the market share of any new drug; if the drug
can only be given to the patient sub-population who passes a companion
diagnostic test, then the drug has a smaller market. However, the cost
of development is increasing exponentially and the chance
of eventual FDA approval is dropping.
Acceptance of a smaller market share in trade for an improved chance of
FDA approval (and possibly higher market penetration) is driving an increasing
willingness in the pharmaceutical industry to develop drugs with companion
diagnostics. Companion diagnostics are
often based on high-throughput molecular data such as DNA mutation, RNA
expression, metabolomics and proteomics.
What is the best way to integrate
high-throughput molecular data with clinical data to ensure the identification
of the optimal subpopulation for a new therapeutic? Can we make the case for a new therapeutic
within the context of the new financial and regulatory incentives faced by
healthcare systems?
Example. The
FDA lists
9 different drugs and 19 different drug – companion diagnostic combinations
that are approved. However, they list
154 drug – gene pairs for which particular versions of the gene lead to
potential adverse events. Some of these
are serious events. For example, some
people have a variant in a gene called CYP2D6 that causes Codeine to be
metabolized into morphine very quickly.
In children, that process can lead to lethal
doses. Unfortunately, identifying
genetic variants that lead to serious adverse events does not automatically
lead to the requirement that the gene be tested before the drug is given. It will be up to providers to decide what is
best for their patients and payers to decide which tests will be reimbursed.
I
have discussed only a few places where the combination of federal regulation,
changing incentives and “big” data are coming together to transform healthcare
as an industry. However, when combined
these constitute large shifts in business models with the potential to leave
companies who stick to old approaches in the dust. It is impossible to know where healthcare in
America is going, but it is clearly going somewhere.
