A groundbreaking, real-world approach to identifying patients to accelerate treatment.

1. PAGE 38 IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS
INSIGHTS COMMERCIAL AND MARKET ACCESS
John Rigg, PHD
Principal and Head of Predictive Analytics, RWE Solutions, IMS Health
John.Rigg@uk.imshealth.com
How predictive analytics can help
find the rare disease patient
Early and accurate diagnosis of diseases is key to optimizing
outcomes but particularly challenging in rare disorders, which
often go undetected for years. new tools leveraging real-world
data and innovation in advanced analytics are creating
opportunities for dramatic improvements in identifying hard-to-find
patients. Pioneering examples in studies of a rare multi-system
disease and a cardiovascular condition demonstrate exciting
potential, signaling a role for their use in broader strategies to
accelerate effective treatment.

2. ACCESSPOINT • VOLUME 6 • ISSUE 11 PAGE 39
Although exact definitions vary, rare diseases are those
affecting a small percentage of people.1
In the EU this is
considered to mean no more than 5 cases per 10,000
individuals, and in the USA fewer than 200,000 individuals
at any one time. But with somewhere in the region of 7,000
rare diseases already identified, collectively their burden is
considerable: there are now an estimated 350 million
sufferers worldwide – more than AIDS and cancer put
together.2
And with, on average, five new rare diseases
being described in the medical literature each week,3
patient numbers continue to grow.
Often genetic and frequently chronic, life-threatening and
debilitating,1,3
most rare diseases cannot be cured.
However, significant developments in precision medicinei
have seen notable breakthroughs in recent years, including
highly targeted therapies addressing causal factors rather
than symptoms alone. With more opportunities for
treatment and over 450 orphan drugsii
in development, the
outlook for many individuals is increasingly hopeful.4
Yet even as progress continues, patients face tremendous
barriers in benefiting from these innovations. In particular
they struggle to obtain a timely and accurate diagnosis,
which is difficult to deliver for the diverse range of rare
disorders with widely varying signs and symptoms.1
Delays arise from misdiagnoses, multiple consultations and
administration of inappropriate interventions (Figure 1).
These delays all too often leave rare diseases undetected
until a stage when even exceptional treatments are less
effective. Given the progressive nature of many rare
diseases, the consequences of late diagnosis can be
immense and include physical deterioration, unnecessary
stress and in some cases death.8
There are also significant
financial implications, both for the individuals involved and
society as a whole, reflecting the multiplicity of physician
visits and greater use of costly diagnostic procedures.9
Furthermore, as observed by Kole and Faurisson,10
late-
stage diagnosis can impede knowledge building in rare
diseases, preventing improved understanding of the early
manifestations of particular conditions.
As a key element of broader efforts to improve awareness
and management of rare diseases, solutions to enable
earlier detection are imperative. In the words of one patient
who experienced a five-year delay in being diagnosed with
sarcoidosis: “As for advice about getting a diagnosis, I wish
I had some magic formula for others.”11
In fact, the
opportunity to find one has never been better.
continued on next page
Identifying patients to accelerate treatment
Diagnosis for Rare Diseases
Based on a European survey
covering 8 rare diseases5
are initially misdiagnosed.
This has resulted in
over 40%
of patients
Patients visit an average of
7.3 physicians
prior to an accurate diagnosis.6 5.6 years6
7.6 years
A patient with a rare disease to
receive the correct diagnosis
waits on average
30%of patients have received three
or more misdiagnoses7
1 out of 6 patients going
through surgery and
1 out of 10 patients receiving
psychiatric treatment
Figure 1: Delays in diagnosis for rare diseases
i
The branch of medicine that enables healthcare services and treatment tailored to the specific genetic makeup of the individual (IMS Health
RWE Dictionary; http://rwedictionary.com/)
iI
Medicinal products intended for diagnosis, prevention or treatment of life-threatening or debilitating rare diseases
(http://www.eurordis.org/about-orphan-drugs)

3. PAGE 40 IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS
INSIGHTS COMMERCIAL AND MARKET ACCESS
a groundbreaking, real-world approach
Alongside the science that is revolutionizing the treatment
of rare diseases are two broader, parallel developments: the
expansion of real-world data (RWD) and innovation in the
analytical methods that can be used to interrogate the data.
When brought together and supported by clinical insight
these allow for the development of screening algorithms,
presenting a major opportunity to help detect new,
undiagnosed patients.
1. Expansion of RWD: The exponential increase in
electronic healthcare information includes rich data
from sources such as primary care Electronic Medical
Records (EMRs), hospitals, claims information and
patient registries. Detailed RWD on symptomology,
diagnoses, consultations, treatment history, lab tests,
etc, is routinely collected from patients and anonymized.
Coupled with the growing ability to integrate data from
different sources, there is now an unparalleled data
foundation for finding undiagnosed patients.
2. Innovation in analytics: The field of advanced analytics
has evolved greatly in industries such as finance and
consumer goods. Sophisticated predictive techniques and
algorithms that revolutionized facial recognition systems
in on-line search engines are now helping to solve
complex problems in healthcare. Machine learningiii
technology, for example, can be used to identify
complex, subtle patterns in the data of diagnosed
patients to assist in detecting new sufferers of a disease.
Pattern recognition techniques leveraging machine
learning have successfully found ‘the needle in the
haystack’ of undiagnosed patients with rare conditions.
Demonstrating dramatic improvements in detection
IMS Health has conducted pioneering research which
demonstrates the power of RWD in helping to solve the
problem of under diagnosis in rare diseases. This is
illustrated in the following two recent case studies: the first
shows how potentially undiagnosed patients can be found
using screening tools based on advanced analytics; the
second how RWD can be used to identify potential health
system barriers to diagnosis. Both approaches are
complementary, tackling patient-level and system-wide
challenges respectively.
Case study 1
Finding patients at high risk of a rare multi-system
disease earlier
Highly promising results from a recent study in a rare
multi-system disease demonstrate the application of a
screening algorithm in the UK. Specifically, the focus was
to detect patients with this particular condition, which is
substantially under diagnosed and where up to 40% of
identified patients are diagnosed late, often by decades.
Potentially undiagnosed patients were identified from
routinely collected primary care and administrative data by
virtue of advanced machine learning methods incorporating
clinical expertise which revealed patterns in the data that
were predictive of disease presence.
The analysis was conducted in two stages leveraging de-
identified EMRs. Firstly, analytics experts developed a
screening algorithm using classical statistical methods
combined with clinical expertise. Secondly, they applied
advanced machine learning methods to refine and optimize
the algorithm.
A test (‘blind’) sample of 70,000 randomly selected
patients was risk-scored by the initial algorithm without
knowing which patients had a confirmed diagnosis for the
disease. This produced a high-risk group containing 8% of
confirmed cases. The test sample was then risk scored by
the refined algorithm, exploiting machine learning
techniques. This produced a prevalence of the confirmed
diagnosis in the highest risk group of 20.5%. Given that
only 0.7% of patients in the test sample actually had the
disease, the evidence suggests that the algorithm could be
used to dramatically increase the odds of finding high-risk
patients earlier (Figure 2).
Confirmed Diagnosis
Potentially
Undiagnosed
Candidates
For Screening
Potential Undiagnosed
Potential Diagnosis
via Algorithm
Diagnosed
Cases
50
50
39
84
45
Source: IMS Health
Figure 2: Potentially undiagnosed patients identified for screening
100
80
60
20
0
40
120
140
160
180
200
220
240
NumberofPatients
Center Carrying Out Diagnostic Procedure
Source: IMS Health
Tertiary
Center
1
Tertiary
Center
2
Tertiary
Center
3
Tertiary
Center
4
Tertiary
Center
5
Other
230
148
72 70
62
39
Number of Patients Diagnosed by Center
Figure 3: Variation in disease incidence by tertiary center
iii
A collection of advanced, data-driven statistical methods which can be used to identify complex patterns in data.

4. ACCESSPOINT • VOLUME 6 • ISSUE 11 PAGE 41
Case study 2
Identifying health system barriers causing under diagnosis
A second case study is an analysis which was conducted to
determine, in a complex, multi-center diagnosis pathway,
whether a lengthy diagnosis process could be a causal
factor in late presentations of a potentially fatal rare
cardiac disease. If detected early, the condition could be
reversible or manageable with treatment.
In a process involving literature and data profiling, a cohort
selection algorithm was developed leveraging secondary
care data in a major EU country. The selected cohort
triangulated well with literature incidence and demographic
values and enabled health service usage and diagnosis
patterns to be investigated for a period of more than five
years (April 2009 to October 2014).
The analysis revealed a high number of events (21 for the
average patient) for three years ahead of a formal
diagnosis, with over 90% of patients being known to the
hospital system within the three-year time frame. It also
demonstrated wide variation in the types of diagnostic
pathways followed to reach a tertiary center initially.
Patients were found to see on average three different
hospital centers in the three years pre-diagnosis and five
different specialty types. Furthermore, the study identified
substantial variability in the incidence rate per 100K
population, being higher in regions feeding in to the
leading diagnosis center (Figure 3). This suggested
challenges of under diagnosis in other parts of the country.
Insights from this research were positively received by
leading clinical experts in the field as a novel and previously
unseen perspective on their patient population. The study
generated hypotheses for further work and served as a basis
for building a rich pool of RWD to inform this therapy area,
in association with academic and clinical institutions.
These studies illustrate the power of techniques enabled by
the use of RWD and analytics to facilitate broader efforts to
reach patients suffering from a rare disease with treatment
that could, potentially, be curative.
Impressive results but challenges remain
Evidence now exists to show how the application of
advanced analytics to large-scale RWD can help identify
undiagnosed patients with rare diseases. These screening
algorithms can form an important part of the portfolio of
strategies to bring the right treatment, including innovative
new therapies, to patients with rare diseases.
The initial results are impressive: the growing availability
of RWD creates a rich foundation for screening algorithms
while developments in advanced machine learning and
predictive analytics, such as signal detection theory, enable
the distinction between ‘signal’ and ‘noise’ to make
algorithms accurate and cost-effective. However, there are
challenges to address before they can be employed to flag
high-risk patients from medical records alone: patient
confidentiality has to be protected; underlying data must be
sufficiently broad to reach a critical mass of these hard-to-
find patients; and clinicians must be willing to embrace
results from screening algorithms.
Nevertheless, if there is serious intent to develop
treatments to improve the lives of patients with rare
diseases, then there should be equally serious efforts to
find those patients. RWD and predictive analytics can
undoubtedly play an important role in helping to achieve
this goal.
1
What is a rare disease? EURORDIS. Rare Diseases Europe. http://www.eurordis.org/content/what-rare-disease
2
Global Genes. Available at: https://globalgenes.org/who-we-are-2/ Accessed 6 Dec 2015
3
Medicines for rare diseases. European Medicines Agency.
http://www.ema.europa.eu/ema/index.jsp?curl=pages/special_topics/general/general_content_000034.jsp
4
PhRMA. A Decade Of Innovation in Rare Diseases 2005-2015. PhRMA, 2015. Available at:
http://www.phrma.org/sites/default/files/pdf/PhRMA-Decade-of-Innovation-Rare-Diseases.pdf Accessed 6 December 2015
5
EURORDIS – Rare Diseases Europe. Survey of the delay in diagnosis for 8 rare diseases in Europe (‘EURORDISCARE2’). Available at:
www.eurordis.org/IMG/pdf/Fact_Sheet_Eurordiscare2.pdf
6
Engel PA, Bagal S, Broback M, Boice N. Physician and patient perceptions regarding physician training in rare diseases: The need for
stronger educational initiatives for physicians. Journal of Rare Disorders, 2013; 1(2): 1-15.
http://www.journalofraredisorders.com/pub/IssuePDFs/Engel.pdf
7
Limb L, Nutt S, Sen A. Experiences of rare diseases: An insight from patients and families. Rare Disease UK. December, 2010. Available at:
http://www.raredisease.org.uk/documents/RDUK-Family-Report.pdf Accessed 6 December, 2015
8
EURORDIS. Voice of 12,000 patients. Experiences and Expectations of Rare Disease Patients on Diagnosis and Care in Europe. A report based
on the EurordisCare2 and EurordisCare3 Surveys. EURORDIS, 2009. Available at: http://www.eurordis.org/publication/voice-12000-patients
9
Rare Disease Impact Report: Insights from patients and the medical community. Shire, April 2013. Available at:
http://www.geneticalliance.org.uk/docs/e-update/rare-disease-impact-report.pdf and https://www.shire.com/newsroom/2013/april/shire-
launches-report Accessed 6 December, 2015
10
Kole A, Faurisson F. Rare diseases social epidemiology: Analysis of inequalities. Advances in Experimental Medicine and Biology, 2010;
686: 223-50
11
Inspire. The road to diagnosis: Stories from patients with rare diseases. 2011. https://www.inspire.com/static/inspire/reports/inspire-
rare-disease-day-report-2011.pdf