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Using predictive analytics and big data to optimize
pharmaceutical outcomes
Inmaculada Hernandez, Pharm.D.,
Ph.D., Department of Pharmacy and
Therapeutics, School of Pharmacy,
University of Pittsburgh, Pittsburgh, PA.
Yuting Zhang, Ph.D., Department of
Health Policy and Management, Graduate
School of Public Health, University of
Pittsburgh, Pittsburgh, PA.
Purpose. The steps involved, the resources needed, and the challenges associated with applying predictive analytics in healthcare are described, with a review of successful applications of predictive analytics
in implementing population health management interventions that target
medication-related patient outcomes.
Summary. In healthcare, the term big data typically refers to large quantities of electronic health record, administrative claims, and clinical trial data
as well as data collected from smartphone applications, wearable devices,
social media, and personal genomics services; predictive analytics refers
to innovative methods of analysis developed to overcome challenges associated with big data, including a variety of statistical techniques ranging
from predictive modeling to machine learning to data mining. Predictive
analytics using big data have been applied successfully in several areas of
medication management, such as in the identification of complex patients
or those at highest risk for medication noncompliance or adverse effects.
Because predictive analytics can be used in predicting different outcomes,
they can provide pharmacists with a better understanding of the risks for
specific medication-related problems that each patient faces. This information will enable pharmacists to deliver interventions tailored to patients’
needs. In order to take full advantage of these benefits, however, clinicians
will have to understand the basics of big data and predictive analytics.
Conclusion. Predictive analytics that leverage big data will become an
indispensable tool for clinicians in mapping interventions and improving
patient outcomes.
Keywords: big data, medication management, pharmaceutical outcomes,
population health management, predictive analytics
Am J Health-Syst Pharm. 2017; 74:1494-500
T
Address correspondence to
Dr. Hernandez (inh3@pitt.edu).
This article is part of a special AJHP
theme issue on population health
management. Contributions to this issue
were coordinated by AJHP Editorial
Advisory Board member Kenneth M.
Shermock, Pharm.D., Ph.D.
Copyright © 2017, American Society of
Health-System Pharmacists, Inc. All rights
reserved. 1079-2082/17/0902-1494.
DOI 10.2146/ajhp161011
1494
AM J HEALTH-SYST PHARM |
he recent shift from payment based
on volume to value has brought the
implementation of outcome-based
reimbursement models to the U.S.
healthcare system. Because chronic
conditions account for over 85% of
total U.S. healthcare costs and 99% of
Medicare spending, most indicators
used to adjust outcome-based reimbursement or payments measure the
quality and efficiency of care as well as
the clinical outcomes of patients with
chronic conditions.1 Interventions
that target pharmaceutical use and
outcomes are of special interest, because improving medication use in the
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context of chronic disease has been
shown to reduce downstream medical
costs and, therefore, total healthcare
spending.2-6 In fact, the Centers for
Medicare and Medicaid Services and
commercial insurers use measures of
appropriateness of medication use in
patients with chronic disease to adjust
payments to providers. For instance,
in the current Medicare accountable
care organization model, several quality measures are related to medication
management, including the use of
angiotensin-converting enzyme inhibitor or angiotensin receptor blocker therapy in coronary artery disease,
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use of b-blockers in heart failure, and
use of antiplatelet therapy in ischemic
vascular disease.7
Population health management
interventions that aim to improve
medication use and pharmaceutical
outcomes can take different forms. For
example, 2 articles in this theme issue
provide recent examples of using electronic health record (EHR) and claims
data in identifying patients who could
benefit most from pharmacist interventions. One of those articles describes how Geisinger Health System
combines EHR and claims data to
identify target patients for its medication therapy disease management
program, including patients who have
a diagnosis of atrial fibrillation and are
using an oral anticoagulant agent, patients with multiple sclerosis, and patients with familial hypercholesterolemia8; the other article describes how
the Greater Rochester Independent
Practice Association developed an algorithm that synthesizes information
from real-time claims data, inpatient
and emergency room admission data,
and laboratory results to identify patients who could benefit most from
a care management team.9 Another
common application of predictive analytics in optimizing pharmaceutical
outcomes is to identify patients who
are most likely to be nonadherent to
medications.10-13 Because of the popularity of this application, a wide range
of commercial predictive analytics
services that predict medication nonadherence are currently available in
the market.14-17 These solutions can be
contracted by health providers to predict which patients are at highest risk
for medication nonadherence so that
specific interventions can be targeted
at those patients to improve treatment
cost-effectiveness.
Regardless of the form population
health management initiatives take,
a key component is the use of electronic patient data in the identification of target patients. Because of the
increased quality and granularity of
electronic patient data and increased
accessibility to that information, the
KEY POINTS
• Predictive analytics can be used
in identifying patients who can
benefit the most from pharmacist interventions and in predicting pharmaceutical outcomes.
• Predictive analytics can provide pharmacists with a better
understanding of the risks of
specific medication-related
problems that each patient
faces, enabling the delivery of
interventions tailored to the patient’s needs.
• As more patient data and better
predictive tools become available, the role of predictive analytics in optimizing medication
use will expand.
data used in identifying target
populations for these programs are
drawn from extensive information
on a large sample of patients; this
information is usually referred to as
big data.
Concepts and terminology
Definition of big data. The term
big data usually refers to extremely
large and complex databases that aggregate information of different types
and scales, that collect data across
time and distance from multiple
sources, and that often require complex data processing applications.
In healthcare, big data may include
EHR, administrative claims, and clinical trial data as well as data collected
from smartphone applications, wearable devices, social media, and personal genomics services.18 A recent literature review defined healthcare big
data sets as those in which the product
of the sample size and the number of
variables is greater than log 7.19 Big
data are therefore characterized by
3 main characteristics: an extremely
large sample size, high heterogeneity,
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and high dimensionality (a large number of variables available for each unit
of observation).
Challenges associated with
analysis of big data. Because big
data have high dimensionality and
are derived from a large sample, the
analysis of big data sets is associated
with some major challenges, such as
noise accumulation and spurious correlation. Noise accumulation is the accumulation of estimation errors when
the prediction is based on a large
number of parameters, which leads to
poor classification or poor prediction.
In other words, when the number of
predictors is too large, the addition
of more predictors does not improve
the predictive power of the model
but only leads to accumulated noise,
which deteriorates the performance of
the model. Spurious correlation refers
to the fact that when a large number
of variables are evaluated, important
variables can be highly correlated
with variables with which they have
no actual relations.20 The basis of this
problem is similar to that of multiple
comparisons in conventional statistics: Standard statistical tests assume
that a single hypothesis is tested, so
when multiple hypotheses are tested
simultaneously, there is a higher probability of a Type I error—an incorrect
rejection of a true null hypothesis (i.e.,
a false positive).21 This phenomenon
can lead to false scientific discoveries
and wrong inference.20
Definition of predictive analytics. These analytic challenges often
make traditional statistical methods
invalid in analyzing big data. The new
methods of analysis developed to
overcome these challenges are often
referred to as predictive analytics.20
Predictive analytics include a variety
of statistical techniques, from predictive modeling to machine learning to
data mining, that analyze historical
and current data to predict future or
other unknown events. Predictive analytics have been widely used in many
fields, including actuarial science,
marketing, finance, retailing, healthcare, and pharmaceutical research.
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As high-quality patient-level data become available to providers and researchers, predictive analytics will be
increasingly used in healthcare.
Big data predictive analytics in
healthcare. To illustrate the application of predictive analytics to healthcare, here we will discuss a recent
study conducted by the authors that
identified risk factors for cardiovascular adverse effects of antidementia
medications.22 In that study, the research group analyzed claims data on
over 160,000 Medicare beneficiaries
with Alzheimer’s disease receiving
antidementia drugs.22 Specifically, the
likelihood of having a cardiovascular
adverse effect was predicted using
data on all medications documented
during the study period and all International Classification of Diseases,
Ninth Revision (ICD-9), diagnosis
codes and Healthcare Common Procedure Coding System codes recorded
in the claims data of each patient.
Construction of a patient-level
data set
Healthcare providers have access to rich patient-level data, such
as medical and medication records,
laboratory values, claims data, and
physician notes. However, these data
files are often stored and coded separately.23 The first step in applying predictive analytics in healthcare is to
construct a unified and often deidentified data set (Figure 1). When predictive analytics are used in population
health management approaches, the
data set is usually constructed at the
patient level. However, different levels of data consolidation may be preferred for other applications of predictive analytics.
Specifically, the construction of the
data set involves the following steps:
(1) cleansing and normalization of the
original data sets so that the information is consistently formatted across
different data sources, (2) aggregation
of multiple data sources into a unified data set at the patient level, (3)
deidentification of the protected information according to Health Insur-
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ance Portability and Accountability
Act privacy rules,24 and (4) validation
of the process to ensure the accuracy
of the data.23 While integrating the different sources of data, it is often useful to keep track of the origin of the
data; this is important because there
is likely to be a repetition of information across different data sources and
because certain algorithms are better
trained in specific types of data sets.
For instance, information on diagnosis or healthcare utilization is likely to
be captured in both EHR and claims
data. The choice of the data source to
use in the development of a model will
depend on the objectives of the analysis. Whereas EHR data may be more
suitable when performing studies
that require granular clinical information, claims data may be preferred in
some applications (e.g., those that require the use of algorithms originally
trained with claims data).
In the study of antidementia drugs
mentioned above,22 pharmacy and
medical claims data from a 5% random
sample of Medicare beneficiaries were
analyzed. The claims data consisted
of a variety of files, including beneficiary files with demographic characteristics and insurance enrollment
information, institutional claims, provider claims, and pharmacy claims.
In putting together the analytic data
set for the study, we first had to identify relevant information contained in
different data files and merge it into
an aggregated data set at the patient
level. We further had to select which
data sources to use in the case of information repeated across data files.
For instance, in defining an adverse
effect, we had to decide whether to
define it on the basis of institutional
claims or provider claims, since both
contain ICD-9 codes. In this case we
used provider claims, because the setting where the adverse effect was diagnosed was not relevant to the study.
Then we extracted all provider claims
for each patient and examined whether any of the ICD-9 codes recorded in
those claims were for adverse effects.
Based on this information, we defined
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a variable (the occurrence of adverse
effects). Through the creation of this
variable, we aggregated claim-level
information into beneficiary-level information, which was then added to a
patient-level data set containing other
relevant patient characteristics.
Preparation for processing of
the input data set
Reduction of dimensionality. A
crucial factor in the successful application of big data predictive analytics
to a specific research question is to account for both the special features of
big data and the main limitations of
predictive analytics, such as spurious
correlations and noise accumulation.
Even if predictive analytic algorithms
can handle a large number of input
predictors, reducing the number of
input dimensions can improve the
performance of models with high dimensionality. The process of dimensionality reduction is based on the
assumption that the available highdimensional data set lies on or near
a lower-dimensional manifold.25 In
other words, it can be assumed that
the available high-dimensional data
represent multiple and indirect measurements of an underlying source.25
In these scenarios, dimensionality reduction techniques can be applied to transform the original highdimensional data set into a compact
lower-dimensionality expression of
that underlying source.
In the aforementioned study,22 we
explored potential interactions between antidementia drugs and concurrent medications for comorbid
conditions whose use could increase
the risk of adverse effects. Because we
hypothesized that interactions would
be similar within therapeutic classes,
we aggregated medication-use information at the therapeutic class level.
Doing this substantially reduced the
number of variables needed in the
model for each patient. In the original claims data set, each medication
was recorded according to active ingredient instead of therapeutic class.
If we had created a variable for each
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Figure 1. Steps in the development of big data–driven predictive analytics in the implementation of population health
management interventions. First, multiple sources of data are aggregated into a unified data set at the patient level. Second, the observations and variables of interest for the specific study question are selected. Third, techniques to reduce
data dimensionality are applied. Fourth, the data set is randomly split into training, validation, and test sets. Fifth, the training set is used for variable selection and model building. Sixth, the validation set is used to calculate performance measures for all models and finalize model selection. Finally, the test set is used to calculate the performance of the selected
model. Blue boxes represent data sets, red boxes represent processes, yellow stars represent the models constructed,
and the purple star represents the model selected. EHR = electronic health record.
Genetic
information
Claims data
Medication
records
Other EHR
information
Laboratory
values
Aggregated
data set
Identification of study sample
Definition of predictors and outcome
Input data set for
study sample
Dimensionality
reduction
Data random
split
Test set
Validation set
Training set
Univariate variable selection
Model building with multivariate
variable selection
Comparison of
models
Models
Selection of final
model
Evaluation of performance
of final model
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active ingredient, we would have had
thousands of variables. By aggregating active ingredients into therapeutic
classes, the number of variables that
represented medication use was decreased from thousands to 242. This
is an example of the application of a
dimensionality reduction technique
called aggregation of variables to a
data set in which the available information (active ingredients) represented multiple measures of an underlying
lower-dimensional source of information (therapeutic classes).
After dimensionality reduction
techniques are applied, there may still
be a large number of predictors. In
such cases, researchers may decide to
eliminate covariates that are unlikely
to carry useful information, such as
those with little variation or low prevalence.26 These techniques are often
considered as a special type of dimensionality reduction technique because
they achieve dimensionality reduction
by creating a subset of the original
number of variables, as opposed to the
previously described techniques, such
as aggregation of variables, which do
not establish subsets of variables but
create new variables that compact the
original information.
In the antidementia drug study,
after using the aggregation of variables technique to reduce the number of covariates, we still had a large
number of covariates (3,077 in this
case).22 To decrease this number, we
excluded covariates with a prevalence
lower than 1%. In other words, we excluded variables for which 99% of the
study participants had the same value.
The rationale behind this process is
that variables that show little variation are unlikely to carry important
information.
Splitting the data set. One of the
biggest limitations of predictive analytics models is their tendency toward
overfitting.20 Overfitting occurs when a
model is excessively complex and describes random error and noise rather
than the underlying relationship.27
The overfitted model represents the
data rather than predicting it. A linear
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model predicts better when applied
to other, similar data sets. In order to
mitigate the overfitting problem, the
original data set can be randomly split
into a training set, a validation set,
and a testing set prior to model building. The training set is used for model
building, the validation set is used for
model selection, and the test set is
used to assess the performance of the
model selected.28
Model building, validation, and
selection. As with conventional statistical methods, the choice of predictive analytics model depends on
the type of outcome variable (e.g.,
categorical versus continuous) and
the research questions. Models commonly used in big data analytics include artificial neuronal networks,
support vector machines, discriminant analysis, and classification trees.
A discussion of the specifics of each
of these algorithms is out of the scope
of this article. Even though these sophisticated models have been shown
to have higher predictive power than
conventional regression models, they
provide little insight into the relative
influence of each parameter or the
effect size.29 For this reason, machine
learning models are often referred
to as “black box” approaches, which
means that they produce predictions
but do not provide an understanding of how they do so. In other words,
they give an estimate of the outcome
for each observation but do not specify the function used in those predictions and do not provide information
on the effect of each covariate. This
limitation is particularly relevant in
healthcare, because clinicians usually
require an explanation of the underlying reasons for model predictions.30
Because of this limitation, a conventional regression model may still be
the best model when estimates of the
impact of each predictor are needed.
For this reason, in the previously
mentioned antidementia study22 we
conducted a logistic regression to
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gression included odds ratios, which
provided information on whether a
variable increases or decreases the
odds of experiencing an adverse effect and by how much. If we had used
other, more advanced techniques
such as neural networks or support
vector machines, we would not have
been able to quantify the impact on
the risk of having an adverse effect of
each predictor.
After the application of the dimensionality reduction techniques to a
given data set, there may still be a large
number of covariates. For instance, in
the study of reference,22 there were
still 650 variables after the application of the dimensionality reduction
techniques mentioned. In such cases,
it is necessary to further decrease the
number of variables before the construction of multivariable regression
models. One way to do this is through
univariate selection. This mechanism
involves the construction of univariate
regressions that measure the strength
of the association between each of
the remaining covariates and the outcome. Then, only the variables that are
strongly associated with the outcome
(those associated with a p value below
a prespecified threshold) are selected
for inclusion in the multivariable regression. In the study of reference, we
conducted 650 univariate regressions
to assess the association between each
of the covariates and the outcome. We
ranked them by p value for univariate
association and selected the top 2.5%
of variables to be included in multivariable models. In the development
of multivariable models in predictive
analytics, it is common to build several models using different algorithms
or different parameters and compare
their performance. For instance, investigators can develop a logistic regression model, a neural network,
and a support vector machine model
using the training set and then select
the best-performing model using the
validation set. Commonly calculated
performance measures include accuracy; area under the curve; specificity; sensitivity; positive and negative
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predictive power for binary outcomes;
and mean absolute error, mean
squared error, and medial absolute error for continuous outcomes. Finally,
the test set is used to calculate the
performance of the selected model,
which predicts the performance of the
model if applied to other data sets.31 In
the antidementia case study, we developed 4 logistic regression models with
varying parameters using the test set
and then selected the top-performing
model using the validation set.
Required resources
Human resources. Different areas of expertise are needed to apply
big data in predictive analytics. First,
a data analyst with expertise in using
different sources of data (e.g., EHR,
claims data, genomics data, data from
wearable devices or social media) is
needed to manage and create analytic
data. Second, a pharmacoepidemiologist or a pharmaceutical outcomes
researcher is needed to develop study
design, define predictors and outcome variables, and interpret results.
Third, depending on the types of
models to be developed, a computer
scientist with some capability in programming languages and advanced
predictive analytics may be needed to
conduct advanced predictive modeling. Finally, a system administrator
may be needed to create and maintain
the infrastructure for data storage and
analyses.
Technology requirements. The
analysis and maintenance of a big data
system require a complex infrastructure, including powerful computing
machines, large storage space, and security protection. Even if storage is relatively inexpensive, the maintenance
of this kind of infrastructure can cost
thousands of dollars per year, especially with the required support of a
system administration staff.32 Instead
of building their own systems, many
organizations, especially small ones
or those with specific needs, rent prebuilt storage and computing resources
at cloud-based data warehouses from
online providers.
Discussion
The use of predictive analytics
in optimizing medication will likely
transform current clinical practice.
Specifically, these techniques will direct pharmacists’ interventions and
other healthcare resources toward
patients whose clinical outcomes
can be improved the most as a consequence of such interventions. Furthermore, because predictive analytics can be used in predicting different
outcomes, pharmacists will have a
better understanding of the risks of
specific medication-related problems
that each patient faces. This information will enable pharmacists to deliver
tailored interventions that target the
medication-related problems that each
patient is most likely to experience. For
example, in a health system where
predictions for medication nonadherence, drug interactions, or adverse
effects are available for each patient,
pharmacists would be able to identify which of these problems are most
relevant to a certain patient and focus
their time on providing recommendations to prevent them. In order to take
full advantage of these benefits, clinicians will have to embrace these predictions in their current clinical practice, which will likely require some
understanding of predictive analytics.
Therefore, it is important to include
the basics of big data and predictive
analytics in the training of healthcare
providers.
In addition to the need to train clinicians in this discipline, the application of predictive analytics in a health
system faces 3 other major limitations.
First, even if predictive techniques are
developed to overcome the limitations
associated with the use of conventional statistics in large databases, their results are still subject to a high chance
of false scientific discoveries; this is
because when a large number of variables are evaluated, some variables
can be highly correlated with others
by chance. Because of the high chance
of false discovery associated with the
use of large databases, and because
of the methodological complexity of
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these methods, healthcare applications that employ predictive analytics
often start as research projects. Once
the methodological and interpretational challenges associated with the
development of predictive analytics
algorithms have been overcome, and
once there is strong evidence that
findings do not represent spurious associations, applications based on predictive analytics are deployed in clinical practice.
Second, the storage and analysis of
large quantities of patient data require
the development and maintenance of
a complex and secure infrastructure
with high computing power, as well
as experts in multiple domains, including data management, computer
science, epidemiology or outcomes
research, and systems administration.
Finally, the conventional procedures
commonly used to assure privacy protection may not be sufficient to prevent patient identification, because
the combination of high data granularity and extensive information on
each observation can enable the reidentification of individuals.32
As more patient data and better
predictive tools become available, the
role of predictive analytics in the optimization of healthcare resources will
expand. The early adopters of these
techniques will gain a competitive advantage among their peers.
Conclusion
Predictive analytics that leverage
big data will become an indispensable tool for clinicians in mapping
interventions and improving patient
outcomes.
Disclosures
The authors have declared no potential
conflicts of interest.
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MainSiteFiles/images/BigDataTaskForceReport_FINAL_2_12_15_b.pdf
(accessed 2016 Nov 16).
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