Medical Concept Representation Learning from Electronic Health
Records and its Application on Heart Failure Prediction

Edward Choi*,MS; Andy Schuetz**,PhD; Walter F. Stewart**,PhD; Jimeng Sun*,PhD

*Georgia Institute of Technology, Atlanta, USA
**Research Development & Dissemination, Sutter Health, Walnut Creek, USA

Corresponding Author: Jimeng Sun
Georgia Institute of Technology
266 Ferst Drive, Atlanta, GA 30313
Tel: 404.894.0482
E-mail: jsun@cc.gatech.edu

Keywords:
Neural Networks, Representation learning, Predictive modeling, Heart Failure prediction.

Word Count: 3600

ABSTRACT
Objective: To transform heterogeneous clinical data from electronic health records into
clinically meaningful constructed features using data driven method that rely, in part, on
temporal relations among data.
Materials and Methods: The clinically meaningful representations of medical concepts
and patients are the key for health analytic applications. Most of existing approaches
directly construct features mapped to raw data (e.g., ICD or CPT codes), or utilize some
ontology mapping such as SNOMED codes. However, none of the existing approaches
leverage EHR data directly for learning such concept representation. We propose a new
way to represent heterogeneous medical concepts (e.g., diagnoses, medications and
procedures) based on co-occurrence patterns in longitudinal electronic health records. The
intuition behind the method is to map medical concepts that are co-occuring closely in time
to similar concept vectors so that their distance will be small. We also derive a simple
method to construct patient vectors from the related medical concept vectors.
Results: For qualitative evaluation, we study similar medical concepts across diagnosis,
medication and procedure. In quantitative evaluation, our proposed representation
significantly improves the predictive modeling performance for onset of heart failure (HF),
where classification methods (e.g. logistic regression, neural network, support vector
machine and K-nearest neighbors) achieve up to 23% improvement in area under the ROC
curve (AUC) using this proposed representation.
Conclusion: We proposed an effective method for patient and medical concept
representation learning. The resulting representation can map relevant concepts together
and also improves predictive modeling performance.

Introduction
Growth in use of electronic health records (EHR) in health care delivery is opening
unprecedented opportunities to predict patient risk, understand what works best for a given
patient, and to personalize clinical decision-making. But, raw EHR data, represented by a
heterogeneous mix of elements (e.g., clinical measures, diagnoses, medications,
procedures) and voluminous unstructured content, may not be optimal for analytic uses or
even for clinical care. While higher order clinical features (e.g., disease phenotypes) are
intuitively more meaningful and can reduce data volume, they may fail to capture
meaningful information inherent to patient data. We explored whether novel data driven
methods that rely on the temporal occurrence of EHR data elements could yield higher
order intuitively interpretable features that both capture pathophysiologic relations inherent
to data and improve performance of predictive models.
Growth in use of EHRs is raising fundamental questions on optimal ways to
represent structured and unstructured data. Medical ontologies such as SNOMED, RxNorm
and LOINC offer structured hierarchical means of compressing data and of understand
relations among data from different domains (e.g., disease diagnosis, labs, prescriptions).
But, these ontologies do not offer the means of extracting meaningful relations inherent to
longitudinal patient data. Scalable methods that can detect pathophysiologic relations
inherent to longitudinal EHR data and construct intuitive features may accelerate more
effective use of EHR data in clinical care and advances in performance of predictive
analytics.
The abstract concepts inherent to existing ontologies does not provide a means to
connect elements in different domains to a common underlying pathophysiologic

constructs that are represented by how data elements co-occur in time. The data driven
approach we developed logically organizes data into higher order constructs.
Heterogeneous medical data were mapped to a low-dimensional space that accounted for
temporal clustering of similar concepts (e.g., A1c lab test, ICD-9 code for diabetes,
prescription for metformin). Co-occurring clusters (e.g., diabetes and peripheral
neuropathy) were then identified and formed into higher order pathophysiologic feature
sets organized by prevalence.
We propose to learn such a medical concept representation on longitudinal EHR
data based on a state-of-the-art neural network model. We also propose an efficient way to
derive patient representation based on the medical concept representation (or medical
concept vectors). We calculated for a set of diseases their closest diseases, medications and
procedures to demonstrate the clinical knowledge captured by the medical concept
representations. We use those learned representation for heart failure prediction tasks,
where significant performance improvement up to 23% in AUC can be obtained on many
classification models (logistic regression: AUC 0.766 to 0.791, SVM: AUC 0.736 to 0.791,
neural network: AUC 0.779 to 0.814, KNN: AUC 0.637 to 0.785)
BACKGROUND
Representation Learning in Natural Language Processing
Recently, neural network based representation learning has shown success in many
fields such as computer vision [1] [2] [3], audio processing [4] [5] and natural language
processing (NLP) [6] [7] [8]. We discuss representation learning in NLP, in particular, as
our proposed method is based on Skip-gram [6] [9], a popular method for learning word
representations.

Mikolov et al. [6] [9] proposed Skip-gram, a simple model based on neural network
that can learn real-valued multi-dimensional vectors that capture relations between words
by training on massive amount of text. The trained real-valued vectors will have similar
values for syntactically and semantically close words such as dog and cat or would and
could, but distinct values for words that are not. Pennington et al. [10] proposed GloVe, a
word representation algorithm based on the global co-occurrence matrix. While GloVe and
Skip-gram essentially achieve the same goal by taking different approaches, GloVe is
computationally faster than Skip-gram as it precomputes co-occurrence information before
the actual learning. Skip-gram, however, requires less number of hyper-parameters to tune
than GloVe, and generally shows better performance [11].
As natural language text can be seen as a sequence of codes, medical records such as
diagnoses, medications and procedures can also be seen as a sequence of codes over time.
In this work, we propose a framework for mapping raw medical concepts (e.g., ICD9, CPT)
into related concept vectors using Skip-gram and validating the utility of the resulting
medical concept vectors.
Representation Learning in the Clinical Field
A few researchers applied representation learning in the clinical field recently.
Minnaro-Gimenez et al. [12] learned the representations of medical terms by applying
Skip-gram to various medical text. They collected the medical text from PubMed, Merck
Manuals, Medscape and Wikipedia. De Vine et al. [13] learned the representations of
UMLS concepts from free-text patient records and medical journal abstracts. The first preprocessed the text to map the words to UMLS concepts, then applied Skip-gram to learn
the representations of the concepts.

More recently, Choi et al. [14] applied Skip-gram to structured dataset from a health
insurance company, where the dataset consisted of patient visit records along with
diagnosis codes(ICD9), lab test results(LOINC), and drug usage(NDC). Their goal, to learn
efficient representations of medical concepts, partially overlaps with our goal. Our study
however, is focused on learning the representations of medical concepts and using them to
generate patient representations, apply them to a real-world prediction problem to
demonstrate improved performance provided by the efficient representation learning.
MATERIALS AND METHODS
Training Phase
Medical Concept
Representation
Learning

EHR

Intensity

Kidney failure
Medical
Mapping
Concept
Vectors

Patient
Representation
Construction

Aggregation

Time

Patient
Vectors

Heart Failure
Prediction Model
Training

Input

Model

Output

Heart Failure
Risk Score

Prediction Phase

Figure 1. Flowchart of the proposed method

Intensity

Kidney failure

In Figure 1, we give a Time
high-level overview of the steps we take to perform HF
prediction. In the training phase, we first train medical concept vectors from the EHR
dataset using Skip-gram. Then, we construct patient representation using the medical
concept vectors. The patient representation is then used to train heart failure prediction
models using various classifiers, namely logistic regression, support vector machine
(SVM), multi-layer perceptron with one hidden layer (MLP) and K-nearest neighbors
classifier (KNN). In the prediction phase, we map the medical record of a patient to medical
concept vectors and generate patient vectors by aggregating the concept vectors. Then we

plug the patient vectors into the trained model, which in turn will generate the risk score
for heart failure.
In the following sections, we will describe medical concept representation learning
and patient representation construction in more detail.
Medical Concept Representation Learning
N-dimensional vector

D-dimensional vector

Bronchitis: [1, 0, 0, 0, 0, …, 0, 0, 0]
Pneumonia: [0, 1, 0, 0, 0, …, 0, 0, 0]
Obesity: [0, 0, 1, 0, 0, …, 0, 0, 0]

Bronchitis: [0.4, -0.2, …, 0.2]
Pneumonia: [0.3, -0.3, …, 0.1]
Obesity: [-0.7, 1.4, …, 1.2]

Cataract:

Cataract:

N diagnoses

[0, 0, 0, 0, 0, …, 0, 0, 1]

(a) One-hot encoding for diagnoses

[1.2, 0.8, …, 1.5]

(b) A better representation of diagnoses

Figure 2. Two different representation of diagnoses. Typically, raw data dimensionality
N(~10,000) is much larger than concept dimensionality D(50~1,000)
Figure 2 depicts a straightforward motivation for using a better representation for
medical concepts. Figure 2(a) shows one-hot encoding of N unique diagnoses using Ndimensional vectors. It is easy to see that this is not an effective representation in that the
difference between Bronchitis and Pneumonia are the same as the difference between
Pneumonia and Obesity. Figure 2(b) shows a better representation in that Bronchitis and
Pneumonia share similar values compared to other diagnoses. By using Skip-gram, we will
be able to better represent not only diagnoses but also medications and procedures as multidimensional real-valued vectors that will capture the latent relations between them.

(a) Patient medical records on a timeline
Pneumonia

Benzonatate

Amoxicillin
Time

Cough

Cough

Benzonatate

Fever

Pneumonia

Chest X-ray

Chest X-ray

Fever
(b) Predicting neighboring medical concepts given Fever
Bezonanate

Fever

Chest X-ray

Amoxicillin

Pneumonia
(c) Predicting neighboring medical concepts given Pneumonia
D-dimensional
v(ct-2)

v(ct-1)

v(ct+1)

v(ct+2)

v(ct)
mapping
ct
(d) Model architecture of Skip-gram

Figure 3. Training examples and the model architecture of Skip-gram

Figure 3(a) is an example of a patient medical record in a temporal order. Skip-gram
assumes the meaning of a concept is determined by its context(or neighbors). Therefore,
given a sequence of concepts, Skip-gram picks a target concept and tries to predict its
neighbors, as shown by Figure 3(b). Then we slide the context window, pick the next target
and do the same context prediction, which is shown by Figure 3(c). Since the goal of Skipgram is to learn the vector representation of concepts, we need to convert medical concepts
to D-dimensional vectors, where D is a user-chosen value typically between 50 and 1000.

Therefore the actual prediction is conducted with vectors, as shown by Figure 3(d), where
c" is the concept at the t-th timestep, 𝐯 𝑐" the vector that represents c" . The goal of Skipgram is to maximize the following average log probability,
1
𝑇

4

log 𝑝 𝑐"+, |𝑐" , where
"56 ./0,0/,,23

𝑝 𝑐"+, |𝑐" =

𝑒𝑥𝑝 𝐯 𝑐"+,
?
@56 𝑒𝑥𝑝

>

𝐯 𝑐"

𝐯 𝑐 > 𝐯 𝑐"

where T is the length of the sequence of medical concepts, w the size of the context window,
𝑐" the target medical concept at timestep t, 𝑐"+, the neighboring medical concept at
timestep t+j, 𝐯 𝑐 the vector that represents the medical concept c, N the total number of
medical concepts. The size of the context window is typically set to 5, giving us 10 concepts
surrounding the target concept. Note that the conditional probability is expressed as a
softmax function. Simply put, by maximizing the softmax score of the inner product of the
neighboring concepts, Skip-gram learns real-valued vectors that efficiently capture the
fine-grained relations between concepts. It needs to be mentioned that our formulation of
Skip-gram is different from the original Skip-gram. In Mikolov et al. [9], they distinguish
the vectors for the target concept and the vectors for the neighboring concept. In our
formulation, we force the two sets of vectors to hold the same values as suggested by [15].
This simpler formulation allowed faster training and impressive results.
Patient Representation Construction
In this section, we describe a simple derivation of patient representation using the
learned medical concept vectors. One of the impressive features of Skip-gram in Mikolov
et al. [9] was that the word vectors supported syntactically and semantically meaningful

linear operations that enabled word analogy calculations such that the resulting vector of
King – Man + Woman is closest to Queen vector.
We expect that the medical concept representations learned by Skip-gram will show similar
properties so that the concept vectors will support clinically meaningful vector additions.
Then, an efficient representation of a patient will be as simple as converting all medical
concepts in his medical history to medical concept vectors, then summing all those vectors
to obtain a single representation vector, as shown in Figure 4. In the experiments, we show
examples of clinically meaningful concept vector additions.
Benzonatate

Pneumonia

Amoxicillin

(a)

Time
Cough

Fever

Chest X-ray

(b)

(c)

Medical concept vectors

+

+

+

+

+

=
Patient representation

Figure 4. Patient representation construction. (a) represents a medical record of a patient
on a timeline. (b) The medical concepts are represented as vectors using the trained medical
concept vectors. (c) The patient is represented as a vector by summing all medical concept
vectors.

EXPERIMENTS AND RESULTS
Population and Source of Data

Data were from Sutter Palo Alto Medical Foundation (Sutter-PAMF) primary care
patients. Sutter-PAMF is a large primary care and multispecialty group practice that has
used an EHR for more than a decade. The study dataset was extracted with cases and
controls identified within the interval from 05/16/2000 to 05/23/2013. The EHR data
included demographics, smoking and alcohol consumptions, clinical and laboratory values,
International Classification of Disease version 9 (ICD-9) codes associated with encounters,
order, and referrals, procedure information in Current Procedural Terminology (CPT)
codes and medication prescription information in medical names. The dataset contained
265,336 patients with 555,609 unique clinical events in total.
Configuration for Medical Concept Representation Learning
To apply Skip-gram, we scanned through encounter, medication order, procedure
order and problem list records of all 265,336 patients, and extracted diagnosis, medication
and procedure codes assigned to each patient in temporal order. If a patient received
multiple diagnoses, medications or procedures at a single visit, then those medical codes
were given the same timestamp. The respective number of unique diagnoses, medications
and procedures was 11,460, 17,769 and 9,370 totaling to 38,599 unique medical concepts.
We used 100-dimensional vectors to represent medical concepts(i.e. D=100 in Figure 2(b)),
considering 300 was sufficient to effectively represent 692,000 vocabularies in NLP. [9]
We used Theano [16], a Python library for evaluating mathematical expression to
implement Skip-gram. Theano can also take advantage of GPUs to greatly improve the
speed of calculations involving large matrices. For optimization, we used Adadelta [17],
which employs adaptive learning rate. Unlike stochastic gradient descent (SGD), which is
widely used for training neural networks, Adadelta does not depend very strongly on the

Diagnosis Codes using SkipGram

ion reducec by t-SNE

ntation of Diagnosis Codes using SkipGram
15by t-SNE of Diagnosis Codes using SkipGram
Dimension
reducec
Vector
Representation
Dimension reducec by t-SNE
10

des using SkipGram

setting of the learning rate, and shows good performance. Using Theano 0.7 and CUDA 7

t-SNE Dimension2

5

Vector Representation of Diagnosis Codes using SkipGram
Dimension reducec by t-SNE
is Codes using SkipGram
on an Ubuntu
machine
E5-2697 and
0
Vector Representation
of Diagnosis
Codeswith
using Xeon
SkipGram
by t-SNE

Nvidia Tesla K80, it took approximately

Dimension reducec by t-SNE

43 hours to run 10 epochs of Adadelta with the batch size of 100.

-5

Evaluation of Medical Concept Representation Learning

-10

Infectious And Parasitic Diseases
Diseases Of The Nervous System And Sense Organs
Neoplasms
Diseases Of The Circulatory System
Endocrine, Nutritional And Metabolic Diseases, And Immunity Disorders
Diseases Of The Respiratory System
Diseases Of The Blood And Blood-Forming Organs
Diseases Of The Digestive System
Vector Representation of Diagnosis Codes using
SkipGram
Mental Disorders
Diseases
Of The Genitourinary System

-15

-20

Dimension reducec by t-SNE

30

-25
25

-30
-30
-10

20

-5

-25

-20

0

-15

5

-10

10

-5

15

0

20

5

10

15

20

25

Highcharts.com

t-SNE Dimension1
Highcharts.com

15

5

-20

-15

-10

10

-5

0

5

10

15

20

25

30

t-SNE Dimension1
Highcharts.com

5

5

-5

0

t-SNE Dimension1

-15

-10

-5

t-SNE Dimension2

0

t-SNE Dimension1

10

15

5 0

20

10

0

25

15

30
Highcharts.com

20

5

25

10

15

30

Highcharts.com
20

25

10

15

30

t-SNE Dimension1

-5

5

Highcharts.com

-10

20

25

30
Highcharts.com

-15

-20

25
ension1

-15

5

-20

-10

10
-15

-5
-20

-25

15

0

t-SNE Dimension1
20
25
-5

-10

5

0

10

30

15

5

20

10

25

15

20

Benign Skin Neoplasms

30
Highcharts.com

Malignant Skin Neoplasms
t-SNE Dimension1
Highcharts.com

25

30
Highcharts.com

-30
-30

30

25 Dimension1
30
t-SNE

-25

-20

-15

-10

-5

0

5

10

15

20

25

30

t-SNE Dimension1
Highcharts.com

053.29: Herpes zoster with other ophthalmic complications
053.21: Herpes zoster keratoconjunctivitis

053.19: Herpes zoster with other nervous system complications

364.04: Secondary iridocyclitis, noninfectious

364.00: Acute and subacute iridocyclitis, unspecified

053.22: Herpes zoster iridocyclitis

364.3: Unspecified iridocyclitis

Figure 5. Diagnosis vectors projected to a 2D space by t-SNE

Figure 5 shows the trained diagnosis vectors plotted in a 2D space, where we used
t-SNE [18] to reduce the dimensions from 100 to 2. t-SNE is a dimensionality reduction
algorithm that was specifically developed for plotting high-dimensional data into a two or
three dimensional space. We randomly chose 1,000 diagnoses from 10 uppermost
categories of ICD-9, which are displayed at the top of the figure. It is readily visible that
diagnoses are generally well grouped by their corresponding categories. However, if
diagnoses from the same category are in fact quite different, they should be apart. This is
shown by the red box and the blue box in Figure 5. Even though they are from the same
neoplasms category, red box indicates a group of malignant skin neoplasms (172.X, 173.X)
while blue box indicates a group of benign skin neoplasms (216.X). Detailed figure of the
red and blue boxes are in the supplementary section. What is more, as the black box shows,
diagnoses from different groups are located close to one another if they are actually related.
In the black box, iridocyclitis and eye infections related to herpes zoster are closely located,
which corresponds to the fact that approximately 43% herpes zoster ophthalmicus (HZO)
patients develop iridocyclitis. [19]
In order to see how well the representation learning captured the relations between
medications and procedures as well as diagnoses, we conduct the following study. We
chose 100 diagnoses that occurred most frequently in the data, obtained for each diagnosis
50 closest vectors in terms of cosine similarity, picked 5 diagnosis, medication and
procedure vectors among the 50 vectors. Table 2 depicts a portion of the entire lis. Note
that some cells contain less than 5 items, which is because there was less than 5 items in
the 50 closest vectors. The entire list is provided in the supplementary section.

Table 1. Examples of diagnoses and their closest medical concepts.
Diagnoses
-Bronchitis, not specified as acute or chronic
Acute upper

(490)

respiratory

-Cough (786.2)

infections

-Acute sinusitis, unspecified (461.9)

(465.9)

-Acute bronchitis (466.0)
-Acute pharyngitis (462)

Medications

Procedures

-Azithromycin 250 mg po tabs

-Pulse oximetry single

-Promethazine-Codeine 6.25-10

-Serv prov during reg sched

mg/5ml po syrp

eve/wkend/hol hrs

-Amoxicillin 500 mg po caps

-Chest PA & lateral

-Fluticasone Propionate 50 mcg/act

-Gyn cytology (pap) pa

na susp

-Influenza vac (flu clinic

-Flonase 50 mcg/act na susp

only) 3+yo pa

-Diabetes mellitus (250.00)

-Metformin hcl 500 mg po tabs

Diabetes

-Mixed hyperlipidemia (272.2)

-Metformin hcl 1000 mg po tabs

mellitus

-Other abnormal glucose (790.29)

-Glucose blood vi strp

(250.02)

-Obesity, unspecified (278.00)

-Lisinopril 10 mg po tabs

-Pure hypercholesterolemia (272.0)

-Lisinopril 20 mg po tabs

-Anemia, unspecified (285.9)

-Furosemide 20 mg po tabs

-Congestive heart failure, unspecified (428.0) -Hydrochlorothiazide 25 mg po tabs

-Diabetic eye exam (no bill)
-Diabetes education, int
-Ophthalmology, int
-Diabetic foot exam (no bill)
-Influenza vac 3+yr (v04.81)
im
-Debridement of nails, 6 or
more

Edema

-Unspecified essential hypertension (401.9)

-Hydrocodone-Acetaminophen 5-500 -OV est pt min serv

(782.3)

-Atrial fibrillation (427.31)

mg po tabs

-Chronic kidney disease, Stage III (moderate) -Cephalexin 500 mg po caps

-ECG and interpretation

(585.3)

-Chest PA & lateral

-Furosemide 40 mg po tabs

-Blepharitis, unspecified (373.00)
Tear film

-Senile cataract, unspecified (366.10)

insufficiency, -Presbyopia (367.4)
unspecified

-Preglaucoma, unspecified (365.00)

(375.15)

-Other chronic allergic conjunctivitis

-Refraction
-Glasses
-Erythromycin 5 mg/gm op oint
-Patanol 0.1 % op soln

(372.14)

Benign
essential
hypertension
(401.1)

-EKG

-Visual field exam extended
-Visual field exam limited
-Referral to ophthalmology,
int
-Ophthalmology, int

-Hyperlipidemia (272.4)

-Hydrochlorothiazide 25 mg po tabs

-Essential hypertension (401.9)

-Atenonol 50 mg po tabs

-Pure hypercholesterolemia (272.0)

-Lisinopril 10 mg po tabs

-Mixed hyperlipidemia (272.2)

-Lisinopril 40 mg po tabs

-Diabetes mellitus (250.00)

-Lisinopril 20 mg po tabs

-ECG and interpretation
-Influenza vac 3+yr im
-Immun admin im/sq/id/perc
1st vac only
-GI, int
-OV est pt lev 3

Evaluation of Medical Concept Vector Additions
Table 2. Vector operations of medical vectors trained by Skip-gram.
Diagnoses

Hypertension (401.9)
+
Obesity (278.0)

-Hyperlipidemia (272.4)

-Hydrochlorothiazide

-Diabetes (250.00)

-Valsartan

-Coronary atherosclerosis (414.00)

-Nifedipine

-Hypertension (401.1)

-Lisinopril

-Chronic kidney disease (585.3)

-Losartan potassium

-Pneumonia (486)
Fever (780.60)
+
Cough (786.2)

Medications

-Acute bronchitis (466.0)
-Acute upper respiratory infections
(465.9)
-Bronchitis (490)
-Acute sinusitis (461.9)

+
Pain in/around Eye (379.91)

-X-ray chest
-Chest PA & Lateral

-Promethazine-codeine

-Pulse oximetry

-Guaifenesin-codeine

-Serv prov during reg sched

-Proair HFA

eve/wkend/hol hrs

-Levofloxacin

-Inhalation Rx for
obstruction MDI/NEB
-Ophthalmology

-Visual discomfort (368.13)

-Glasses

-Regular astigmatism (367.21)

-Erythromycin ointment -Referral to ophthalmology

-Presbyopia (367.4)

-Patanol

-Blepharitis (373.00)

Loss of Weight (783.21)
+
Anxiety State (300.00)

+
Speech Disturbance (784.59)

-Peripheral refraction
-Visual field exam
-Diabetic eye exam

-Depressive disorder (311)

-Lorazepam

-Referral to GI

-Malais & fatigue (780.79)

-Zolpidem tartrate

-ECG & Interpretation

-Insomnia (780.52)

-Omeprazole

-GI

-Generalized anxiety disorder (300.02)

-Alprazolam

-EKG

-Esophageal reflux (530.81)

-Trazodone HCL

-Chest PA & Lateral

-Dysarthria (784.51)
Hallucination (780.1)

N/A

-Azithromycin

-Tear film insufficiency (375.15)
Visual Disturbance (368.8)

Procedures

-Secondary parkinsonism (332.1)
-Senile dementia with delirium (290.3)
-Mental disorder (294.9)
-Paranoid state (297.9)

-Referral to geriatrics
-Midorine HCL

-Mental status exam

-Risperdal

-Referral to

-Rivastigmine Tartrate

neuropsychology

-Rivastigmine

-Referral to speech therapy
Home visit est pt lev 2

Due to the difficulty of generating medically interesting examples, we chose 5
intuitive examples as shown by the first column of Table 3 to give a simple demonstration
of the medical concept vector additions. We again generated 50 closest vectors to the sum
of two medical concept vectors and picked 5 from each diagnosis, medication and
procedure category.

Setup for Heart Failure Prediction Evaluation
In this section, we first describe why we chose heart failure (HF) prediction task as an
application. Then we briefly mention the models to use, followed by the description of the
data processing steps to create the training data for all models. Lastly, the evaluation
strategy will be followed by implementation details.
Heart failure prediction task: Onset of HF is associated with a high level of disability,
health care costs and mortality (roughly ~50% risk of mortality within 5 years of diagnosis).
[20] [21] There has been relatively little progress in slowing the progression of HF severity,
largely because it is difficult to detect before actual diagnosis. As a consequence,
intervention has primarily been confined to the time period after diagnosis, with little or no
impact on disease progression. Earlier detection of HF could lead to improved outcomes
through patient engagement and more assertive treatment with angiotensin converting
enzyme (ACE)-inhibitors or Angiotensin II receptor blockers (ARBs), mild exercise, and
reduced salt intake, and possibly other options [22] [23] [24] [25].
Models for performance comparison: We aim to emphasize the effectiveness of the
medical concept representation and the patient representation derived from it. Therefore
we trained four popular classifiers, namely logistic regression, MLP, SVM, and KNN using
both one-hot vectors and medical concept vectors.
Definition of Cases and Controls: Criteria for incident onset of HF, are described in [26]
and were adopted from [27]. The criteria are defined as: 1) Qualifying ICD-9 codes for HF
appeared as a diagnosis code in either the encounter, the problem list, or the medication
order fields. Qualifying ICD-9 codes are listed in the supplementary section. Qualifying
ICD-9 codes with image and other related orders were excluded because these orders often

represent a suspicion of HF, where the results are often negative; 2) a minimum of three
clinical encounters with qualifying ICD-9 codes had to occur within 12 months of each
other, where the date of diagnosis was assigned to the earliest of the three dates. If the time
span between the first and second appearances of the HF diagnostic code was greater than
12 months, the date of the second encounter was used as the first qualifying encounter; 3)
ages 50 or greater to less than 85 at the time of HF diagnosis.
Up to ten (nine on average) eligible primary care clinic-, sex-, and age-matched (in
5-year age intervals) controls were selected for each incident HF case. Primary care
patients were eligible as controls if they had no HF diagnosis in the 12-month period before
diagnosis of the incident HF case. Control subjects were required to have their first office
encounter within one year of the matching HF case patient’s first office visit, and have at
least one office encounter 30 days before or any time after the case’s HF diagnosis date to
ensure similar duration of observations among cases and controls.
From 265,336 Sutter-PAMF patients, 3,884 incident HF cases and 28,903 control
patients were identified.
Data processing: To train the four models, we generated the dataset again from the
encounter, medication order, procedure order and problem list records of 3,884 cases and
28,903 controls. Based on the HF diagnosis date (HFDx) of each patient, we extracted all
records from the 18-month period before the HFDx. To train the models with medical
concept vectors, we converted the medical records to patient vectors as shown in Figure 4.
To train the models with one-hot encoding, we converted the medical records to aggregated
one-hot vectors in the same fashion as Figure 4, using one-hot vectors instead of medical
concept vectors.

In order to study the relation between the medical concept vectors trained with
different sizes of data and their influence on the models’ prediction performance, we used
three kinds of medical concept vectors: 1) The one trained with only HF cases (3,884
patients), 2) The one trained with HF cases and controls (32,787 patients), 3) The one
trained with the full sample (265,336 patients). Note that medical concept vectors trained
with smaller number of patients cover less number of medical concepts. Therefore, when
converting patient records to patient vectors as Figure 4, we excluded all medical codes
that did not have matching medical concept vectors. All input vectors were normalized to
zero mean and unit variance.
Evaluation strategy: We used six-fold cross validation to train and evaluate all models,
and to estimate how well the models will generalize to independent datasets. Prediction
performance was measured using area under the ROC curve (AUC), on data not used in
the training. We used the confidence score to calculate its AUC for SVM. Detailed
explanation of the cross validation is given in the supplementary section.
Implementation details: Logistic regression and MLP were implemented with Theano
and trained with Adadelta. SVM and KNN were implemented with Python Scikit-Learn.
All models were trained by the same machine used for medical concept representation
learning. Hyper-parameters used for training each model are described in the
supplementary section.
Evaluation of Heart Failure Prediction

One-hot encoding

Medical concept vectors trained on 4K patients

Medical concept vectors trained on 33K patients

Medical concept vecotrs trained on 265K patients

1
0.95
0.9
0.85

AUC

0.8
0.75
0.7
0.65
0.6
0.55
0.5
Logistic Regression

SVM

MLP

KNN

Figure 6. Heart failure prediction performance of various models and input vectors.

Figure 6 shows the average AUC of 6-fold cross validations of various models and
input vectors. The colors represent different training input vectors. The error bars indicate
the standard deviation derived from the 6-fold cross evaluation. The power of medical
concept representation learning is evident as all models show significant improvement in
the HF prediction performance. Logistic regression and SVM, both being linear models,
show similar performance when trained with medical concept vectors, although SVM
benefits slightly more from using the better representation of medical concepts. MLP also
benefits from using medical concept vectors, and, being a non-linear model, shows better
performance compared to logistic regression and SVM. It is interesting that KNN benefits
the most from using the medical concept vectors, even the ones trained on the smallest

dataset. Considering the fact that KNN classification is based on the distances between data
points, this is a clear indication that proper medical concept representation can alleviate the
sparsity problem induced by the simple one-hot encoding.
Figure 6 also tells us that medical concept representation is best learned with a large
dataset as shown by Mikolov et al. [6] However, in most models, especially KNN, even
the medical concept vectors trained with the smallest number of patients improves the
prediction performance. It is quite surprising given the fact that we used less amount of
information by excluding unmatched medical codes when using medical concept vectors
trained with a small number of patients, the models still show better prediction performance.
This again is a clear proof that medical concept representation learning provides more
effective way to represent medical concepts than one-hot encoding.

Table 3. Training speed improvement. (*Since KNN does not require training, we display
classification time instead)

One-hot
encoding
Medical
concept vectors
Speed -up

Logistic
regression

SVM

MLP

KNN*

81.3

20.5

85.7

2900.82

5.3

1.9

5.6

36.66

x15.3

x10.8

x15.3

x79.1

Table 4 depicts the training time for each model when using one-hot encoding and
medical concept vectors. Considering the high-dimensionality of one-hot encoding,
training the models with medical concept vectors should provide significant speed-up, as
shown by the last row of Table 4. This shows that medical concept vectors not only improve
performance, but also significantly reduce the training time.

Before discussing future work, we would like to emphasize the fact that our entire
experiments were conducted completely without expert knowledge such as medical
ontologies or features designed by medical experts. Using only the medical order records,
we were able to produce clinically meaningful representation of medical concepts. This is
an inspiring discovery that can be extended for numerous other medical problems.
Future Work
Although medical concept vectors have shown impressive results, it would be even
more effective if deeper medical information could be embedded such as lab results or
patient demographic information. This would enable us to represent the medical state of
patients more accurately.
Using expert knowledge is another thing we should try. Even though we have
shown impressive performance only by using medical records, this does not mean we
cannot benefit from well-established expert medical knowledge, such as specific features
or medical ontologies.
Another natural extension of our work is to address other medical problems.
Although this work focused on the early detection of heart failure, our approach is very
general that it could be applied to any kind of disease prediction problem. And the medical
concept vectors can also be used in numerous medical applications as well.
CONCLUSION
We proposed a new way of representing heterogeneous medical concepts as realvalued vectors and constructing efficient patient representation using the state-of-the-art
Deep Learning method. We have qualitatively shown that the trained medical concept
vectors indeed captured medical insights compatible with our medical knowledge and

experience. For the heart failure prediction task, medical concept vectors improved the
performance of many classifiers, thus quantitatively proving its effectiveness. We
discussed the limitation of our method and possible future works, which include deeper
utilization of medical information, combining expert knowledge into our framework, and
expanding our approach to various medical applications.

References
[1] Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, "Imagenet classification with
deep convolutional neural networks," in Advances in Neural Information Processing
Systems, 2012, pp. 1097-1105.
[2] Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol,
"Extracting and composing robust features with denoising autoencoders," in
International Conference on Machine learning, 2008, pp. 1096-1103.
[3] Quoc Le et al., "uilding high-level features using large scale unsupervised learning,"
in arXiv:1112.6209 , 2012.
[4] Honglak Lee, Peter Pham, Yan Largman, and Andrew Ng, "Unsupervised feature
learning for audio classification using convolutional deep belief networks," in
Advances in Neural Information Processing Systems, 2009, pp. 1096-1104.
[5] Geoffrey Hinton et al., "Deep Neural Networks for Acoustic Modeling in Speech
Recognition: The Shared Views of Four Research Groups," Signal Processing
Magazine, vol. 29, no. 6, pp. 82-97, 2012.
[6] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean, "Efficient estimation of
word representations in vector space," in arXiv preprint arXiv:1301.3781, 2013.

[7] Kyunghyun Cho et al., "Learning phrase representations using rnn encoder-decoder
for statistical machine translation," in arXiv preprint arXiv:1406.1078, 2014.
[8] Richard Socher, Jeffrey Pennington, Eric Huang, Andrew Ng, and Christopher
Manning, "Semi-supervised recursive autoencoders for predicting sentiment
distributions," in Empirical Methods in Natural Language Processing, 2011, pp. 151161.
[9] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeff Dean,
"Distributed representations of words and phrases and their compositionality," in
Advances in Neural Information Processing Systems, 2013, pp. 3111-3119.
[10] Jeffrey Pennington, Richard Socher, and Christopher Manning, "Glove: Global
vectors for word representation," in Empirical Methods on Natural Language
Processing, 2014, pp. 1532-1543.
[11] Radim

Rehurek.

(2014,

Dec.)

Rare

Technologies.

[Online].

http://rare-

technologies.com/making-sense-of-word2vec/
[12] Jose Minarro-Gimenez, Oscar Marin-Alonso, and Matthias Samwald, "Exploring the
application of deep learning techniques on medical text corpora," Studies in health
technology and informatics, vol. 205, pp. 584-588, 2013.
[13] Lance De Vine, Guido Zuccon, Bevan Koopman, Laurianne Sitbon, and Peter Bruza,
"Medical Semantic Similarity with a Neural Language Model," in International
Conference on Information and Knowledge Management, 2014, pp. 1819-1822.
[14] Youngduk Choi, Chill Chiu, and David Sontag, Learning low-dimensional
representations of medical concepts, 2016, to be appeared in AMIA-CRI.

[15] Xin Rong, "word2vec Parameter learning explained," in arXiv preprint
arXiv:1411.2738, 2014.
[16] James Bergstra et al., "Theano: a CPU and GPU Math Expression Compiler," in
Python for Scientific Computing Conference, 2010.
[17] Matthew Zeiler, "ADADELTA: An adaptive learning rate method," in arXiv preprint
arXiv:1212.5701, 2012.
[18] Laurens Van der Maaten and Geoffrey Hinton, "Visualizing data using t-SNE,"
Journal of Machine Learning Research, vol. 9, no. 11, pp. 2579-2605, 2008.
[19] Janice Thean, Anthony Hall, and Richard Stawell, "Uveitis in herpes zoster
ophthalmicus," Clinical & Experimental Ophthalmology, vol. 29, no. 6, pp. 406-410,
2001.
[20] Veronique L. Roger et al., "Trends in heart failure incidence and survival in a
community-based population," JAMA, vol. 292, no. 3, pp. 344-350, July 2004.
[21] Sherry L. Murphy, Jiaquan Xu, and Kenneth D. Kochanek, "Deaths: final data for
2010," National Vital Stat Rep, vol. 61, no. 4, pp. 1-117, May 2010.
[22] SOLVD Investigators, "Effect of enalapril on mortality and the development of heart
failure in asymptomatic patients with reduced left ventricular ejection fractions," N
Engl j Med, vol. 327, pp. 685-691, 1992.
[23] J Arnold et al., "Prevention of heart failure in patients in the Heart Outcomes
Prevention Evaluation (HOPE) study," Circulation, vol. 107, no. 9, pp. 1284-1290,
2003.

[24] Sebastiano Sciarretta, Francesca Palano, Giuliano Tocci, Rossella Baldini, and
Massimo Volpe, "Antihypertensive treatment and development of heart failure in
hypertension: a Bayesian network meta-analysis of studies in patients with
hypertension and high cardiovascular risk," Archives of internal medicine, vol. 171,
no. 5, pp. 384-394, 2011.
[25] Chao-Hung Wang, Richard Weisel, Peter Liu, Paul Fedak, and Subodh Verma,
"Glitazones and heart failure critical appraisal for the clinician," Circulation, vol.
107, no. 10, pp. 1350-1354, 2003.
[26] Rajakrishnan Vijayakrishnan et al., "Prevalence of heart failure signs and symptoms
in a large primary care population identified through the use of text and data mining
of the electronic health record," Journal of Cardiac Failure, vol. 20, no. 7, pp. 459464, 2014.
[27] Jerry Gurwitz et al., "Contemporary prevalence and correlates of incident heart
failure with preserved ejection fraction," The American journal of medicine, vol. 126,
no. 5, pp. 393-400, 2013.

SUPPLEMENTARY
Table 4. Qualifying ICD-9 codes for heart failure
ICD-9
Code
398.91
402.01
402.11
402.91
404.01
404.03
404.11
404.13
404.91
404.93
428.0
428.1
428.20
428.21
428.22
428.23
428.30
428.31
428.32
428.33
428.40
428.41
428.42
428.43
428.9

Description
Rheumatic heart failure (congestive)
Malignant hypertensive heart disease with heart failure
Benign hypertensive heart disease with heart failure
Unspecified hypertensive heart disease with heart failure
Hypertensive heart and chronic kidney disease, malignant, with heart failure and with
chronic kidney disease stage I through stage IV, or unspecified
Hypertensive heart and chronic kidney disease, malignant, with heart failure and with
chronic kidney disease stage V or end stage renal disease
Hypertensive heart and chronic kidney disease, benign, with heart failure and with
chronic kidney disease stage I through stage IV, or unspecified
Hypertensive heart and chronic kidney disease, benign, with heart failure and chronic
kidney disease stage V or end stage renal disease
Hypertensive heart and chronic kidney disease, unspecified, with heart failure and with
chronic kidney disease stage I through stage IV, or unspecified
Hypertensive heart and chronic kidney disease, unspecified, with heart failure and
chronic kidney disease stage V or end stage renal disease
Congestive heart failure, unspecified
Left heart failure
Systolic heart failure, unspecified
Acute systolic heart failure
Chronic systolic heart failure
Acute on chronic systolic heart failure
Diastolic heart failure, unspecified
Acute diastolic heart failure
Chronic diastolic heart failure
Acute on chronic diastolic heart failure
Combined systolic and diastolic heart failure, unspecified
Acute combined systolic and diastolic heart failure
Chronic combined systolic and diastolic heart failure
Acute on chronic combined systolic and diastolic heart failure
Heart failure, unspecified

Red and Blue Box of Figure 5
173.71

173.31
172.5

173.41
173.51

173.50

173.91

172.4

173.6
172.3
173.4
173.0
173.7

238.9
173.2
173.3

173.5

173.1

173.9

Figure 7. Detailed version of the red box of Figure 5

Table 5. List of ICD-9 codes that appear in Figure 7, and their descriptions
ICD-9 Code
172.3
172.4
172.5
173.0
173.1
173.2
173.3
173.31
173.4
173.41
173.5
173.50
173.51
173.6
173.7
173.71
173.9
173.91

Description
Malignant melanoma of skin of other and unspecified parts of face
Malignant melanoma of skin of scalp and neck
Malignant melanoma of skin of trunk, except scrotum
Other and unspecified malignant neoplasm of skin of lip
Other and unspecified malignant neoplasm of skin of eyelid, including canthus
Other and unspecified malignant neoplasm of skin of ear and external auditory canal
Other and unspecified malignant neoplasm of skin of other and unspecified parts of face
Basal cell carcinoma of skin of other and unspecified parts of face
Other and unspecified malignant neoplasm of scalp and skin of neck
Basal cell carcinoma of scalp and skin of neck
Other and unspecified malignant neoplasm of skin of trunk, except scrotum
Unspecified malignant neoplasm of skin of trunk, except scrotum
Basal cell carcinoma of skin of trunk, except scrotum
Other and unspecified malignant neoplasm of skin of upper limb, including shoulder
Other and unspecified malignant neoplasm of skin of lower limb, including hip
Basal cell carcinoma of skin of lower limb, including hip
Other and unspecified malignant neoplasm of skin, site unspecified
Basal cell carcinoma of skin, site unspecified

238.9

Neoplasm of uncertain behavior, site unspecified

448.9
228.00

216.5

216.7

216.6
216.9

216.3

078.10
216.8

238.2
448.1

Figure 8. Detailed version of the blue box of Figure 5

Table 6. List of ICD-9 codes that appear in Figure 8, and their descriptions
ICD-9 Code
078.10
216.3
216.5
216.6
216.7
216.8
216.9
228.00
238.2
448.1
448.9

Description
Viral warts, unspecified
Benign neoplasm of skin of other and unspecified parts of face
Benign neoplasm of skin of trunk, except scrotum
Benign neoplasm of skin of upper limb, including shoulder
Benign neoplasm of skin of lower limb, including hip
Benign neoplasm of other specified sites of skin
Benign neoplasm of skin, site unspecified
Hemangioma of unspecified site
Neoplasm of uncertain behavior of skin
Nevus, non-neoplastic
Other and unspecified capillary diseases

6-fold Cross Validation Scheme

Training Set
Validation Set
Test Set

32,787 Patients

Fold 1

Fold 2

Fold 6

Figure 9. Diagram of 6-fold cross validation

Figure 6 depicts the 6-fold cross validation we performed for HF prediction. As
explained earlier, the entire cohort is divided into 7 chunks, and two chunks take turn to
play as the validation set and the test set.

Hyper-parameters used for training models
After experimenting with various values, the following hyper-parameter setting produced
the best performance. We used Theano 0.7 and CUDA 7 for training logistic regression,
MLP, and GRU models. SVM was implemented with Scikit-Learn Linear SVC. KNN was
implemented with Scikit-Learn KNeighborsClassifier.

Table 7. Hyper-parameter settings for training the models
Model
Logistic regression,
one-hot vectors
Logistic regression,
medical concept vectors
SVM,
one-hot vectors
SVM,
medical concept vectors
MLP,
one-hot vectors
MLP,
medical concept vectors
KNN,
one-hot vectors
KNN,
medical concept vectors

Hyper-parameter
L2 regularization: 0.1, Max epoch: 100
L2 regularization: 0.01, Max epoch: 100
L2 regularization: 0.000001, Dual: False
L2 regularization: 0.001, Dual: False
L2 regularization: 0.01, Hidden layer size: 15, Max epoch: 100
L2 regularization: 0.001, Hidden layer size: 100, Max epoch: 100
Number of neighbors: 15
Number of neighbors: 100

Complete table of 100 frequent diagnoses and their closest diagnoses, medications and
procedures
Diagnoses

Medications

Procedures

Myalgia and myositis,
unspecified (729.1)

-Lumbago (724.2)
-Thoracic or lumbosacral
neuritis or radiculitis,
unspecified (724.4)
-Degeneration of lumbar or
lumbosacral intervertebral
disc (722.52)
-Other malaise and fatigue
(780.79)

-PR INJ TRIGGER POINT(S)
1 OR 2 MUSCLES
-PHYSICAL MEDICINE, INT
-TRIAMCINOLONE
ACETONIDE *KENALOG
INJ
-ASP/INJ MAJOR
JOINT/BURS/CYST
-PR METHLYPRED ACET
(BU 21 - 40MG) *DEPOMEDROL INJ

Pain in limb (729.5)

-Keratoderma, acquired
(701.1)
-Plantar fascial
fibromatosis (728.71)
-Other specified diseases of
nail (703.8)
-Disturbance of skin
sensation (782.0)
-Senile cataract,
unspecified (366.10)
-Presbyopia (367.4)
-Preglaucoma, unspecified
(365.00)
-Other chronic allergic
conjunctivitis (372.14)

-HYDROCODONEACETAMINOPHEN 5-500 MG
PO TABS
-ZOLPIDEM TARTRATE 10
MG PO TABS
-CYCLOBENZAPRINE HCL
10 MG PO TABS
-HYDROCODONEACETAMINOPHEN 10-325
MG PO TABS
-TRAMADOL HCL 50 MG PO
TABS
-HYDROCODONEACETAMINOPHEN 5-500 MG
PO TABS

-GLASSES
-ERYTHROMYCIN 5 MG/GM
OP OINT
-PATANOL 0.1 % OP SOLN

-PR REFRACTION
-PR VISUAL FIELD EXAM
EXTENDED
-PR VISUAL FIELD EXAM
LIMITED
-REFERRAL TO
OPHTHALMOLOGY, INT
-OPHTHALMOLOGY, INT
-REFERRAL TO
NEUROLOGY, INT
-NEUROLOGY, INT
-SERV PROV DURING REG
SCHED EVE/WKEND/HOL
HRS
-PR OV EST PT LEV 3
-PR ECG AND
INTERPRETATION

Tear film insufficiency,
unspecified (375.15)

Headache (784.0)

-Dizziness and giddiness
(780.4)
-Cervicalgia (723.1)
-Other malaise and fatigue
(780.79)
-Acute sinusitis,
unspecified (461.9)

Disturbance of skin
sensation (782.0)

-Brachial neuritis or
radiculitis NOS (723.4)
-Cervicalgia (723.1)
-Pain in limb (729.5)
-Headache (784.0)

Edema (782.3)

-Congestive heart failure,
unspecified (428.0)
-Unspecified essential
hypertension (401.9)
-Atrial fibrillation (427.31)
-Chronic kidney disease,
Stage III (moderate)
(585.3)

-HYDROCODONEACETAMINOPHEN 5-500 MG
PO TABS
-FLUTICASONE
PROPIONATE 50 MCG/ACT
NA SUSP
-CYCLOBENZAPRINE HCL
10 MG PO TABS
-FLONASE 50 MCG/ACT NA
SUSP
-AZITHROMYCIN 250 MG PO
TABS
-HYDROCODONEACETAMINOPHEN 5-500 MG
PO TABS
-PREDNISONE 20 MG PO
TABS

-FUROSEMIDE 20 MG PO
TABS
-HYDROCHLOROTHIAZIDE
25 MG PO TABS
-HYDROCODONEACETAMINOPHEN 5-500 MG
PO TABS
-CEPHALEXIN 500 MG PO
CAPS

-DEBRIDEMENT OF NAILS,
6 OR MORE
-PODIATRY, INT
-REFERRAL TO PODIATRIC
MEDICINE/SURGERY, INT
-PR OV EST PT LEV 3
-FOOT COMPLETE

-NEUROLOGY, INT
-SENSE/MIXED N
CONDUCTION TST
-REFERRAL TO
NEUROLOGY, INT
-PR NERVE COND STUDY
MOTOR W F-WAVE EA
-PR EMG NEEDLE ONE
EXTREMITY
-DEBRIDEMENT OF NAILS,
6 OR MORE
-PR OV EST PT MIN SERV
-EKG
-PR ECG AND
INTERPRETATION
-CHEST PA & LATERAL

Asthma, unspecified type,
unspecified (493.90)

-Allergic rhinitis, cause
unspecified (477.9)
-Acute bronchitis (466.0)
-Bronchitis, not specified as
acute or chronic (490)
-Acute upper respiratory
infections of unspecified
site (465.9)

Routine gynecological
examination (V72.31)

-Other screening
mammogram (V76.12)
-Screening for lipoid
disorders (V77.91)
-Routine general medical
examination at a health care
facility (V70.0)
-Screening for unspecified
condition (V82.9)
-Mixed hyperlipidemia
(272.2)
-Other abnormal glucose
(790.29)
-Obesity, unspecified
(278.00)
-Pure hypercholesterolemia
(272.0)

Diabetes mellitus without
mention of complication,
type II or unspecified type,
uncontrolled (250.02)

Need for prophylactic
vaccination and inoculation
against diphtheria-tetanuspertussis, combined [DTP]
[DTaP] (V06.1)

Diabetes mellitus without
mention of complication,
type II or unspecified type,
not stated as uncontrolled
(250.00)

Personal history of colonic
polyps (V12.72)

-Laboratory examination
ordered as part of a routine
general medical
examination (V72.62)
-Need for prophylactic
vaccination and inoculation
against other combinations
of diseases (V06.8)
-Screening for lipoid
disorders (V77.91)
-Need for prophylactic
vaccination and inoculation
against influenza (V04.81)
-Unspecified essential
hypertension (401.9)
-Diabetes mellitus without
mention of complication,
type II or unspecified type,
uncontrolled (250.02)
-Obesity, unspecified
(278.00)
-Benign essential
hypertension (401.1)
-Special screening for
malignant neoplasms of
colon (V76.51)
-Family history of
malignant neoplasm of
gastrointestinal tract
(V16.0)
-Diverticulosis of colon
(without mention of
hemorrhage) (562.10)
-Routine general medical
examination at a health care
facility (V70.0)

-FUROSEMIDE 40 MG PO
TABS
-AZITHROMYCIN 250 MG PO
TABS
-ALBUTEROL SULFATE HFA
108 (90 BASE) MCG/ACT IN
AERS
-ALBUTEROL SULFATE HFA
108 MCG/ACT IN AERS
-PROMETHAZINE-CODEINE
6.25-10 MG/5ML PO SYRP
-ALBUTEROL 90 MCG/ACT
IN AERS
-OBTAINING SCREEN PAP
SMEAR
-GYN CYTOLOGY (PAP) PA
-MAMMOGRAM SCREENING
-MAMMO SCREENING
BILATERAL DIGITAL
-SCRN MAMMO DIR DIG
IMAGE BIL (V76.12)
-METFORMIN HCL 500 MG
PO TABS
-METFORMIN HCL 1000 MG
PO TABS
-GLUCOSE BLOOD VI STRP
-LISINOPRIL 10 MG PO TABS
-LISINOPRIL 20 MG PO TABS
-FLUTICASONE
PROPIONATE 50 MCG/ACT
NA SUSP
-AZITHROMYCIN 250 MG PO
TABS
-ZOLPIDEM TARTRATE 10
MG PO TABS
-SIMVASTATIN 20 MG PO
TABS

-METFORMIN HCL 500 MG
PO TABS
-METFORMIN HCL 1000 MG
PO TABS
-GLUCOSE BLOOD VI STRP
-HYDROCHLOROTHIAZIDE
25 MG PO TABS
-LISINOPRIL 10 MG PO TABS
-PEG-KCL-NACL-NASULFNA ASC-C 100 G PO SOLR
-MOVIPREP 100 G PO SOLR
-SIMVASTATIN 20 MG PO
TABS
-OMEPRAZOLE 20 MG PO
CPDR

-CHEST PA & LATERAL
-PULSE OXIMETRY SINGLE
-PR INHALATION RX FOR
OBSTRUCTION MDI/NEB
-XR CHEST 2 VIEWS PA
LATERAL
-IMMUN ADMIN
IM/SQ/ID/PERC 1ST VAC
ONLY

-DIABETIC EYE EXAM (NO
BILL)
-DIABETES EDUCATION,
INT
-OPHTHALMOLOGY, INT
-DIABETIC FOOT EXAM
(NO BILL)
-INFLUENZA VAC 3+YR
(V04.81) IM
-TDAP VAC 11-64 YO
*ADACEL (V06.8) IM
-MAMMO SCREENING
BILATERAL DIGITAL
-PR INFLUENZA VACC
PRES FREE 3+ YO 0.5ML IM
-REFERRAL TO GI, INT
-PNEUMOCOCCAL VAC
(V03.82) IM/SQ

-DIABETIC EYE EXAM (NO
BILL)
-PR DIABETIC EYE EXAM
(NO BILL)
-INFLUENZA VAC 3+YR
(V04.81) IM
-IMMUN ADMIN
IM/SQ/ID/PERC 1ST VAC
ONLY
-OPHTHALMOLOGY, INT
-PR COLONOSCOPY
W/BIOPSY(S)
-PR COLONOSCOPY
W/RMVL
LESN/TUM/POLYP SNARE
-REFERRAL TO GI, INT
-GI, INT
-PR COLONOSCOPY DIAG

Acute upper respiratory
infections of unspecified
site (465.9)

-Cough (786.2)
-Acute sinusitis,
unspecified (461.9)
-Acute bronchitis (466.0)
-Acute pharyngitis (462)

Need for prophylactic
vaccination and inoculation
against tetanus-diphtheria
[Td] (DT) (V06.5)

-Routine general medical
examination at a health care
facility (V70.0)
-Other general medical
examination for
administrative purposes
(V70.3)
-Need for prophylactic
vaccination and inoculation
against other combinations
of diseases (V06.8)
-Multiphasic screening
(V82.6)
-Unspecified essential
hypertension (401.9)
-Allergic rhinitis, cause
unspecified (477.9)
-Cough (786.2)
-Routine general medical
examination at a health care
facility (V70.0)
-Lens replaced by other
means (V43.1)
-Senile cataract,
unspecified (366.10)
-Unspecified aftercare
(V58.9)
-Follow-up examination,
following surgery,
unspecified (V67.00)
-Irritable bowel syndrome
(564.1)
-Nausea with vomiting
(787.01)
-Hemorrhage of rectum and
anus (569.3)
-Nausea alone (787.02)

Esophageal reflux (530.81)

Other specified aftercare
following surgery (V58.49)

Diarrhea (787.91)

Coronary atherosclerosis of
unspecified type of vessel,
native or graft (414.00)

-Unspecified essential
hypertension (401.9)
-Atrial fibrillation (427.31)
-Benign essential
hypertension (401.1)
-Congestive heart failure,
unspecified (428.0)

Dizziness and giddiness
(780.4)

-Benign paroxysmal
positional vertigo (386.11)
-Other malaise and fatigue
(780.79)
-Unspecified hearing loss
(389.9)
-Chest pain, unspecified
(786.50)
-Unspecified hearing loss
(389.9)
-Infective otitis externa,

Impacted cerumen (380.4)

-AZITHROMYCIN 250 MG PO
TABS
-PROMETHAZINE-CODEINE
6.25-10 MG/5ML PO SYRP
-AMOXICILLIN 500 MG PO
CAPS
-FLUTICASONE
PROPIONATE 50 MCG/ACT
NA SUSP
-FLONASE 50 MCG/ACT NA
SUSP
-TD VAC 7+ YO (V06.5) IM
-IMMUN ADMIN
IM/SQ/ID/PERC 1ST VAC
ONLY
-CHART ABSTRACTION
SIMP (V70.3)
-MAMMOGRAM SCREENING
-IMMUN ADMIN, EACH ADD

-PULSE OXIMETRY SINGLE
-SERV PROV DURING REG
SCHED EVE/WKEND/HOL
HRS
-CHEST PA & LATERAL
-GYN CYTOLOGY (PAP) PA
-INFLUENZA VAC (FLU
CLINIC ONLY) 3+YO PA

-OMEPRAZOLE 20 MG PO
CPDR
-ACIPHEX 20 MG PO TBEC
-PROTONIX 40 MG PO TBEC
-PRILOSEC 20 MG PO CPDR
-FLUTICASONE
PROPIONATE 50 MCG/ACT
NA SUSP
-GLASSES
-HYDROCODONEACETAMINOPHEN 5-500 MG
PO TABS
-VICODIN 5-500 MG PO TABS
-CEPHALEXIN 500 MG PO
CAPS
-IBUPROFEN 600 MG PO
TABS
-METRONIDAZOLE 500 MG
PO TABS
-CIPRO 500 MG PO TABS
-OMEPRAZOLE 20 MG PO
CPDR
-CIPROFLOXACIN HCL 500
MG PO TABS
-PROMETHAZINE HCL 25
MG PO TABS
-SIMVASTATIN 40 MG PO
TABS
-NITROGLYCERIN 0.4 MG SL
SUBL
-SIMVASTATIN 20 MG PO
TABS
-LISINOPRIL 10 MG PO TABS
-LISINOPRIL 20 MG PO TABS
-LORAZEPAM 0.5 MG PO
TABS
-MECLIZINE HCL 25 MG PO
TABS
-FLUTICASONE
PROPIONATE 50 MCG/ACT
NA SUSP

-GI, INT
-PR ECG AND
INTERPRETATION
-PR LARYNGOSCOPY FLEX
DIAG
-PR OV EST PT LEV 3
-CHEST PA & LATERAL

-NEOMYCIN-POLYMYXINHC 1 % OT SOLN
-AMOXICILLIN 500 MG PO

-PR ECG AND
INTERPRETATION
-PR OPHTHALMIC
BIOMETRY
-FOOT COMPLETE
-EKG
-PR REFRACTION
-GI, INT
-REFERRAL TO GI, INT
-SERV PROV DURING REG
SCHED EVE/WKEND/HOL
HRS
-PR COLONOSCOPY
W/BIOPSY(S)
-US ABDOMEN COMPLETE
-PR ECG AND
INTERPRETATION
-EKG
-STRESS ECHO
-TTE W/O DOPPLER CMPLT
-PR CARDIAC STRESS
COMPLTE
-PR ECG AND
INTERPRETATION
-PR TYMPANOMETRY
(IMPEDANCE TESTING)
-EKG
-PR COMPREHENSIVE
HEARING TEST
-ENT, INT
-PR REMVL EAR WAX
UNI/BILAT (380.4)
-ENT, INT

unspecified (380.10)
-Dysfunction of Eustachian
tube (381.81)
-Dizziness and giddiness
(780.4)

CAPS
-FLONASE 50 MCG/ACT NA
SUSP
-FLUTICASONE
PROPIONATE 50 MCG/ACT
NA SUSP
-TRIAMCINOLONE
ACETONIDE 0.1 % EX CREA
-FLUOCINONIDE 0.05 % EX
CREA

Other chronic dermatitis
due to solar radiation
(692.74)

-Personal history of other
malignant neoplasm of skin
(V10.83)
-Other seborrheic keratosis
(702.19)
-Benign neoplasm of other
specified sites of skin
(216.8)
-Inflamed seborrheic
keratosis (702.11)

Chronic kidney disease,
Stage III (moderate)
(585.3)

-Anemia, unspecified
(285.9)
-Diabetes with renal
manifestations, type II or
unspecified type, not stated
as uncontrolled (250.40)
-Proteinuria (791.0)
-Unspecified essential
hypertension (401.9)
-Unspecified essential
hypertension (401.9)
-Other and unspecified
hyperlipidemia (272.4)
-Long-term (current) use of
other medications (V58.69)
-Screening for malignant
neoplasms of prostate
(V76.44)
-Keratoderma, acquired
(701.1)
-Peripheral vascular
disease, unspecified (443.9)
-Ingrowing nail (703.0)
-Diabetes with neurological
manifestations, type II or
unspecified type, not stated
as uncontrolled (250.60)
-Tear film insufficiency,
unspecified (375.15)
-Glaucomatous atrophy
[cupping] of optic disc
(377.14)
-Vitreous degeneration
(379.21)
-Lens replaced by other
means (V43.1)

-AMLODIPINE BESYLATE 10
MG PO TABS
-FUROSEMIDE 20 MG PO
TABS
-AMLODIPINE BESYLATE 5
MG PO TABS
-LISINOPRIL 40 MG PO TABS
-HYDROCHLOROTHIAZIDE
25 MG PO TABS
-COLCHICINE 0.6 MG PO
TABS
-ALLOPURINOL 100 MG PO
TABS
-ALLOPURINOL 300 MG PO
TABS
-INDOMETHACIN 50 MG PO
CAPS
-ATENOLOL 50 MG PO TABS
-ECONAZOLE NITRATE 1 %
EX CREA

-Pre-operative
cardiovascular examination
(V72.81)
-Other specified preoperative examination
(V72.83)
-Unspecified aftercare
(V58.9)
-Senile cataract,
unspecified (366.10)
-Acute upper respiratory
infections of unspecified
site (465.9)
-Bronchitis, not specified as
acute or chronic (490)

-HYDROCODONEACETAMINOPHEN 5-500 MG
PO TABS
-VICODIN 5-500 MG PO TABS
-IBUPROFEN 600 MG PO
TABS

Gout, unspecified (274.9)

Other specified diseases of
nail (703.8)

Preglaucoma, unspecified
(365.00)

Pre-operative examination,
unspecified (V72.84)

Cough (786.2)

-GLASSES

-AZITHROMYCIN 250 MG PO
TABS
-PROMETHAZINE-CODEINE
6.25-10 MG/5ML PO SYRP
-ALBUTEROL SULFATE HFA

-PR COMPREHENSIVE
HEARING TEST
-PR TYMPANOMETRY
(IMPEDANCE TESTING)
-CERUMEN RMVL BY
NURSE/MA (NO BILL)
-PR DESTR PRE-MALIG
LESN 1ST
-PR DESTR PRE-MALIG
LESN 2-14
-PR BIOPSY OF
SKIN/SQ/MUC MEMBR
LESN SINGLE
-PR DESTRUCT BENIGN
LESION UP TO 14 LESIONS
(LIQUID NITROGEN)
-PR OV EST PT LEV 3
-PR PROTHROMBIN TIME
-PR TX/ PROPHY/ DX INJ
SQ/ IM
-PR OV EST PT MIN SERV
-EKG
-DEBRIDEMENT OF NAILS,
6 OR MORE
-DEBRIDEMENT OF NAILS,
6 OR MORE
-TRIAMCINOLONE
ACETONIDE *KENALOG
INJ

-DEBRIDEMENT OF NAILS,
6 OR MORE
-TRIM SKIN LESIONS, 2 TO
4
-PARE CORN/CALLUS,
SINGLE LESION
-PODIATRY, INT
-PR DESTR PRE-MALIG
LESN 1ST
-PR VISUAL FIELD EXAM
EXTENDED
-PR FUNDAL
PHOTOGRAPHY
-PR OPHTHALMIC DX
IMAGING
-PR VISUAL FIELD EXAM
LIMITED
-PR CMPTR OPHTH IMG
OPTIC NERVE
-PR ECG AND
INTERPRETATION
-EKG
-CHEST PA & LATERAL
-PR OV EST PT LEV 3
-ECG AND
INTERPRETATION
(ORDERS ONLY) SZ
-CHEST PA & LATERAL
-XR CHEST 2 VIEWS PA
LATERAL
-PULSE OXIMETRY SINGLE
-SERV PROV DURING REG

-Asthma, unspecified type,
unspecified (493.90)
-Acute sinusitis,
unspecified (461.9)

Palpitations (785.1)

Need for prophylactic
vaccination and inoculation
against influenza (V04.81)

Benign essential
hypertension (401.1)

Unspecified essential
hypertension (401.9)

Other screening
mammogram (V76.12)

Elevated blood pressure
reading without diagnosis
of hypertension (796.2)

-Cardiac dysrhythmia,
unspecified (427.9)
-Syncope and collapse
(780.2)
-Dizziness and giddiness
(780.4)
-Shortness of breath
(786.05)
-Other and unspecified
hyperlipidemia (272.4)
-Need for prophylactic
vaccination and inoculation
against diphtheria-tetanuspertussis, combined [DTP]
[DTaP] (V06.1)
-Unspecified essential
hypertension (401.9)
-Screening for unspecified
condition (V82.9)
-Unspecified essential
hypertension (401.9)
-Pure hypercholesterolemia
(272.0)
-Mixed hyperlipidemia
(272.2)
-Diabetes mellitus without
mention of complication,
type II or unspecified type,
not stated as uncontrolled
(250.00)
-Diabetes mellitus without
mention of complication,
type II or unspecified type,
not stated as uncontrolled
(250.00)
-Benign essential
hypertension (401.1)
-Coronary atherosclerosis
of unspecified type of
vessel, native or graft
(414.00)
-Obesity, unspecified
(278.00)
-Screening for malignant
neoplasms of cervix
(V76.2)
-Routine general medical
examination at a health care
facility (V70.0)
-Screening for unspecified
condition (V82.9)
-Special screening for
malignant neoplasms of
colon (V76.51)
-Screening for unspecified
condition (V82.9)
-Impaired fasting glucose
(790.21)
-Family history of ischemic
heart disease (V17.3)
-Obesity, unspecified
(278.00)

108 (90 BASE) MCG/ACT IN
AERS
-FLUTICASONE
PROPIONATE 50 MCG/ACT
NA SUSP
-GUAIFENESIN-CODEINE
100-10 MG/5ML PO SYRP
-ATENOLOL 25 MG PO TABS
-METOPROLOL SUCCINATE
ER 25 MG PO TB24
-LORAZEPAM 0.5 MG PO
TABS

SCHED EVE/WKEND/HOL
HRS
-PR ECG AND
INTERPRETATION

-AZITHROMYCIN 250 MG PO
TABS
-FLUTICASONE
PROPIONATE 50 MCG/ACT
NA SUSP
-SIMVASTATIN 20 MG PO
TABS
-HYDROCHLOROTHIAZIDE
25 MG PO TABS

-PR INFLUENZA VACC
PRES FREE 3+ YO 0.5ML IM
-INFLUENZA VAC 3+YR
(V04.81) IM
-INFLUENZA VAC (FLU
CLINIC ONLY) 3+YO PA
-IMMUN ADMIN
IM/SQ/ID/PERC 1ST VAC
ONLY
-TDAP VAC 11-64 YO
*ADACEL (V06.8) IM
-PR ECG AND
INTERPRETATION
-INFLUENZA VAC 3+YR
(V04.81) IM
-IMMUN ADMIN
IM/SQ/ID/PERC 1ST VAC
ONLY
-GI, INT
-PR OV EST PT LEV 3

-HYDROCHLOROTHIAZIDE
25 MG PO TABS
-ATENOLOL 50 MG PO TABS
-LISINOPRIL 10 MG PO TABS
-LISINOPRIL 40 MG PO TABS
-LISINOPRIL 20 MG PO TABS

-HOLTER 24 HR ECG
-PR ECG AND
INTERPRETATION
-PR CARDIAC STRESS
COMPLTE
-EKG
-TTE W/O DOPPLER CMPLT

-HYDROCHLOROTHIAZIDE
25 MG PO TABS
-LISINOPRIL 10 MG PO TABS
-ATENOLOL 50 MG PO TABS
-LISINOPRIL 20 MG PO TABS
-SIMVASTATIN 20 MG PO
TABS

-PR ECG AND
INTERPRETATION
-EKG
-INFLUENZA VAC 3+YR
(V04.81) IM
-IMMUN ADMIN
IM/SQ/ID/PERC 1ST VAC
ONLY

-FLUTICASONE
PROPIONATE 50 MCG/ACT
NA SUSP

-MAMMO SCREENING
BILATERAL DIGITAL
-MAMMOGRAM
SCREENING
-GYN CYTOLOGY (PAP) PA
-OBTAINING SCREEN PAP
SMEAR
-PR OV EST PT LEV 3

-AZITHROMYCIN 250 MG PO
TABS

-PR OV EST PT LEV 3
-PR OV EST PT LEV 2
-CHART ABSTRACTION
SIMP (V70.3)
-PR ECG AND
INTERPRETATION
-TD VAC 7+ YO (V06.5) IM

Symptomatic menopausal
or female climacteric states
(627.2)

Congestive heart failure,
unspecified (428.0)

Other and unspecified
hyperlipidemia (272.4)

Pure hypercholesterolemia
(272.0)

Mixed hyperlipidemia
(272.2)

Unspecified acquired
hypothyroidism (244.9)

Depressive disorder, not
elsewhere classified (311)

-Other screening
mammogram (V76.12)
-Routine gynecological
examination (V72.31)
-Screening for malignant
neoplasms of cervix
(V76.2)
-Disorder of bone and
cartilage, unspecified
(733.90)
-Edema (782.3)
-Coronary atherosclerosis
of unspecified type of
vessel, native or graft
(414.00)
-Aortic valve disorders
(424.1)
-Mitral valve disorders
(424.0)
-Diabetes mellitus without
mention of complication,
type II or unspecified type,
not stated as uncontrolled
(250.00)
-Benign essential
hypertension (401.1)
-Coronary atherosclerosis
of unspecified type of
vessel, native or graft
(414.00)
-Need for prophylactic
vaccination and inoculation
against influenza (V04.81)
-Benign essential
hypertension (401.1)
-Other and unspecified
hyperlipidemia (272.4)
-Impaired fasting glucose
(790.21)
-Diabetes mellitus without
mention of complication,
type II or unspecified type,
uncontrolled (250.02)
-Benign essential
hypertension (401.1)
-Diabetes mellitus without
mention of complication,
type II or unspecified type,
uncontrolled (250.02)
-Impaired fasting glucose
(790.21)
-Obesity, unspecified
(278.00)
-Unspecified essential
hypertension (401.9)
-Benign essential
hypertension (401.1)
-Other screening
mammogram (V76.12)
-Other malaise and fatigue
(780.79)
-Insomnia, unspecified
(780.52)
-Other malaise and fatigue
(780.79)
-Unspecified essential
hypertension (401.9)

-ZOLPIDEM TARTRATE 10
MG PO TABS

-MAMMOGRAM
SCREENING
-MAMMO SCREENING
BILATERAL DIGITAL
-GYN CYTOLOGY (PAP) PA
-PR OV EST PT LEV 3
-OBTAINING SCREEN PAP
SMEAR

-FUROSEMIDE 20 MG PO
TABS
-FUROSEMIDE 40 MG PO
TABS
-LASIX 20 MG PO TABS
-LASIX 40 MG PO TABS
-LISINOPRIL 5 MG PO TABS

-TTE W/O DOPPLER CMPLT
-PR OV EST PT MIN SERV
-TRANSTHORACIC
COMPLETE, ECHO
-CHEST PA & LATERAL
-PR PROTHROMBIN TIME

-SIMVASTATIN 20 MG PO
TABS
-SIMVASTATIN 40 MG PO
TABS
-HYDROCHLOROTHIAZIDE
25 MG PO TABS
-LISINOPRIL 10 MG PO TABS
-LIPITOR 10 MG PO TABS

-PR ECG AND
INTERPRETATION
-IMMUN ADMIN
IM/SQ/ID/PERC 1ST VAC
ONLY
-PR OV EST PT LEV 3
-INFLUENZA VAC 3+YR
(V04.81) IM
-PR INFLUENZA VACC
PRES FREE 3+ YO 0.5ML IM

-SIMVASTATIN 20 MG PO
TABS
-SIMVASTATIN 40 MG PO
TABS
-LISINOPRIL 10 MG PO TABS
-ATENOLOL 50 MG PO TABS
-HYDROCHLOROTHIAZIDE
25 MG PO TABS

-EKG
-PR ECG AND
INTERPRETATION
-TDAP VAC 11-64 YO
*ADACEL (V06.8) IM
-MAMMO SCREENING
BILATERAL DIGITAL

-SIMVASTATIN 40 MG PO
TABS
-SIMVASTATIN 20 MG PO
TABS
-LISINOPRIL 10 MG PO TABS
-SIMVASTATIN 10 MG PO
TABS
-METFORMIN HCL 500 MG
PO TABS

-TDAP VAC 11-64 YO
*ADACEL (V06.8) IM
-INFLUENZA VAC 3+YR
(V04.81) IM
-EKG
-PR CARDIAC STRESS
COMPLTE

-LEVOTHYROXINE SODIUM
75 MCG PO TABS
-HYDROCHLOROTHIAZIDE
25 MG PO TABS
-LEVOTHYROXINE SODIUM
100 MCG PO TABS
-SIMVASTATIN 20 MG PO
TABS
-SIMVASTATIN 40 MG PO
TABS
-ZOLPIDEM TARTRATE 10
MG PO TABS
-LORAZEPAM 0.5 MG PO
TABS
-HYDROCHLOROTHIAZIDE
25 MG PO TABS
-TRAZODONE HCL 50 MG PO

-MAMMO SCREENING
BILATERAL DIGITAL
-IMMUN ADMIN
IM/SQ/ID/PERC 1ST VAC
ONLY
-PR OV EST PT LEV 3
-MAMMOGRAM
SCREENING
-PR INFLUENZA VACC
PRES FREE 3+ YO 0.5ML IM
-IMMUN ADMIN
IM/SQ/ID/PERC 1ST VAC
ONLY
-PR OV EST PT LEV 3
-INFLUENZA VAC 3+YR
(V04.81) IM

-Other and unspecified
hyperlipidemia (272.4)
Personal history of other
malignant neoplasm of skin
(V10.83)

Bronchitis, not specified as
acute or chronic (490)

-Other chronic dermatitis
due to solar radiation
(692.74)
-Benign neoplasm of other
specified sites of skin
(216.8)
-Other seborrheic keratosis
(702.19)
-Neoplasm of uncertain
behavior of skin (238.2)
-Acute upper respiratory
infections of unspecified
site (465.9)
-Cough (786.2)
-Acute sinusitis,
unspecified (461.9)
-Acute nasopharyngitis
[common cold] (460)

Obstructive sleep apnea
(adult)(pediatric) (327.23)

-Sleep disturbance,
unspecified (780.50)
-Obesity, unspecified
(278.00)
-Other respiratory
abnormalities (786.09)
-Other malaise and fatigue
(780.79)

Anemia, unspecified
(285.9)

-Chronic kidney disease,
Stage III (moderate)
(585.3)
-Edema (782.3)
-Unspecified essential
hypertension (401.9)
-Iron deficiency anemia,
unspecified (280.9)
-Tear film insufficiency,
unspecified (375.15)
-Vitreous degeneration
(379.21)
-Regular astigmatism
(367.21)
-Other specified aftercare
following surgery (V58.49)

Lens replaced by other
means (V43.1)

Chest pain, unspecified
(786.50)

-Palpitations (785.1)
-Other chest pain (786.59)
-Other respiratory
abnormalities (786.09)
-Other malaise and fatigue
(780.79)

Acute sinusitis, unspecified
(461.9)

-Cough (786.2)
-Bronchitis, not specified as
acute or chronic (490)
-Allergic rhinitis, cause
unspecified (477.9)
-Acute pharyngitis (462)

TABS
-HYDROCODONEACETAMINOPHEN 5-500 MG
PO TABS
-FLUOCINONIDE 0.05 % EX
CREA
-TRIAMCINOLONE
ACETONIDE 0.1 % EX CREA

-PROMETHAZINE-CODEINE
6.25-10 MG/5ML PO SYRP
-AZITHROMYCIN 250 MG PO
TABS
-ZITHROMAX Z-PAK 250 MG
PO TABS
-ALBUTEROL 90 MCG/ACT
IN AERS
-ALBUTEROL SULFATE HFA
108 MCG/ACT IN AERS
-ZOLPIDEM TARTRATE 10
MG PO TABS
-FLUTICASONE
PROPIONATE 50 MCG/ACT
NA SUSP
-SIMVASTATIN 20 MG PO
TABS
-ZOLPIDEM TARTRATE 5
MG PO TABS
-AZITHROMYCIN 250 MG PO
TABS
-HYDROCHLOROTHIAZIDE
25 MG PO TABS

-GLASSES

-AZITHROMYCIN 250 MG PO
TABS
-NITROGLYCERIN 0.4 MG SL
SUBL
-HYDROCODONEACETAMINOPHEN 5-500 MG
PO TABS
-OMEPRAZOLE 20 MG PO
CPDR
-AMOXICILLIN 500 MG PO
CAPS
-AZITHROMYCIN 250 MG PO
TABS
-FLONASE 50 MCG/ACT NA
SUSP
-PROMETHAZINE-CODEINE
6.25-10 MG/5ML PO SYRP

-MAMMO SCREENING
BILATERAL DIGITAL
-PR DESTR PRE-MALIG
LESN 1ST
-PR DESTR PRE-MALIG
LESN 2-14
-PR BIOPSY OF
SKIN/SQ/MUC MEMBR
LESN SINGLE
-SURGICAL PATHOLOGY
PA
-PR OV EST PT LEV 3
-PULSE OXIMETRY SINGLE
-CHEST PA & LATERAL
-PR INHALATION RX FOR
OBSTRUCTION MDI/NEB
-SERV PROV DURING REG
SCHED EVE/WKEND/HOL
HRS
-XR CHEST 2 VIEWS PA
LATERAL
-REFERRAL TO SLEEP
DISORDERS, INT
-PR POLYSOMNOGRAPHY
4 OR MORE
-PR POLYSOMNOGRAPHY
W/C P
-SLEEP STUDY ATTENDED
-CPAP EQUIPMENT DME

-INFLUENZA VAC 3+YR
(V04.81) IM
-PR ECG AND
INTERPRETATION
-GI, INT
-EKG
-CHEST PA & LATERAL
-PR VISUAL FIELD EXAM
EXTENDED
-PR REFRACTION
-PR OPHTHALMIC DX
IMAGING
-DIABETIC EYE EXAM (NO
BILL)
-PR DIABETIC EYE EXAM
(NO BILL)
-PR ECG AND
INTERPRETATION
-PR CARDIAC STRESS
COMPLTE
-EKG
-STRESS ECHO
-CHEST PA & LATERAL
-PULSE OXIMETRY SINGLE
-SERV PROV DURING REG
SCHED EVE/WKEND/HOL
HRS
-CHEST PA & LATERAL
-IMMUN ADMIN
IM/SQ/ID/PERC 1ST VAC

Atrial fibrillation (427.31)

Urinary tract infection, site
not specified (599.0)

-Encounter for therapeutic
drug monitoring (V58.83)
-Coronary atherosclerosis
of unspecified type of
vessel, native or graft
(414.00)
-Congestive heart failure,
unspecified (428.0)
-Unspecified essential
hypertension (401.9)
-Dysuria (788.1)
-Urinary frequency
(788.41)
-Other malaise and fatigue
(780.79)
-Vaginitis and
vulvovaginitis, unspecified
(616.10)

Pain in joint, shoulder
region (719.41)

-Other affections of
shoulder region, not
elsewhere classified (726.2)
-Rotator cuff (capsule)
sprain (840.4)
-Cervicalgia (723.1)
-Pain in joint, lower leg
(719.46)

Encounter for therapeutic
drug monitoring (V58.83)

-Atrial fibrillation (427.31)
-Congestive heart failure,
unspecified (428.0)
-Coronary atherosclerosis
of unspecified type of
vessel, native or graft
(414.00)

Malignant neoplasm of
breast (female), unspecified
(174.9)

-Lump or mass in breast
(611.72)
-Unspecified aftercare
(V58.9)
-Disorder of bone and
cartilage, unspecified
(733.90)
-Pre-operative examination,
unspecified (V72.84)

Contact dermatitis and
other eczema, unspecified
cause (692.9)

-Unspecified disorder of
skin and subcutaneous
tissue (709.9)
-Other atopic dermatitis
and related conditions
(691.8)
-Other seborrheic keratosis
(702.19)
-Other chronic dermatitis
due to solar radiation
(692.74)
-Special screening for
malignant neoplasms of
colon (V76.51)
-Diverticulosis of colon
(without mention of

Benign neoplasm of colon
(211.3)

-FLUTICASONE
PROPIONATE 50 MCG/ACT
NA SUSP
-WARFARIN SODIUM 5 MG
PO TABS
-ATENOLOL 50 MG PO TABS
-ATENOLOL 25 MG PO TABS
-METOPROLOL SUCCINATE
ER 50 MG PO TB24
-SIMVASTATIN 40 MG PO
TABS

ONLY
-ENT, INT

-NITROFURANTOIN
MONOHYD MACRO 100 MG
PO CAPS
-CIPROFLOXACIN HCL 250
MG PO TABS
-SULFAMETHOXAZOLETMP DS 800-160 MG PO
TABS
-MACROBID 100 MG PO
CAPS
-CIPRO 500 MG PO TABS
-HYDROCODONEACETAMINOPHEN 5-500 MG
PO TABS
-VICODIN 5-500 MG PO TABS
-NAPROXEN 500 MG PO
TABS

-SERV PROV DURING REG
SCHED EVE/WKEND/HOL
HRS
-PR URINALYSIS AUTO
W/O SCOPE
-PR ECG AND
INTERPRETATION
-CYSTOURETHROSCOPY
-PR URINALYSIS DIP W/O
SCOPE

-WARFARIN SODIUM 5 MG
PO TABS
-COUMADIN 5 MG PO TABS
-WARFARIN SODIUM 2.5 MG
PO TABS
-ATENOLOL 50 MG PO TABS
-WARFARIN SODIUM 2 MG
PO TABS
-LORAZEPAM 1 MG PO
TABS
-LORAZEPAM 0.5 MG PO
TABS
-TAMOXIFEN CITRATE 20
MG PO TABS
-VICODIN 5-500 MG PO TABS
-ANASTROZOLE 1 MG PO
TABS
-TRIAMCINOLONE
ACETONIDE 0.1 % EX CREA
-FLUOCINONIDE 0.05 % EX
CREA
-TRIAMCINOLONE
ACETONIDE 0.1 % EX OINT
-DESONIDE 0.05 % EX CREA
-FLUTICASONE
PROPIONATE 50 MCG/ACT
NA SUSP
-PEG-KCL-NACL-NASULFNA ASC-C 100 G PO SOLR
-MOVIPREP 100 G PO SOLR
-OMEPRAZOLE 20 MG PO
CPDR

-PR OV EST PT MIN SERV
-PR PROTHROMBIN TIME
-EKG
-PR ECG AND
INTERPRETATION
-TTE W/O DOPPLER CMPLT

-SHOULDER 2+ VIEWS
-ASP/INJ MAJOR
JOINT/BURS/CYST
-TRIAMCINOLONE
ACETONIDE *KENALOG
INJ
-UPPER EXTREM JOINT
MRI
-PR THERAPEUTIC
EXERCISES
-PR OV EST PT MIN SERV
-PR PROTHROMBIN TIME
-DEBRIDEMENT OF NAILS,
6 OR MORE
-PR DESTR PRE-MALIG
LESN 2-14
-PR ECG AND
INTERPRETATION
-MAMMOGRAM BILAT
DIAGNOSTIC
-DEXA BONE DENSITY
AXIAL SKELETON
HIPS/PELV/SPINE
-MAMMO DIAGNOSTIC
BILATERAL DIGITAL
-VENIPUNC
FNGR,HEEL,EAR
-REFERRAL TO
ONCOLOGY, INT
-DERMATOLOGY, INT
-PR BIOPSY OF
SKIN/SQ/MUC MEMBR
LESN SINGLE
-PR DESTR PRE-MALIG
LESN 1ST
-REFERRAL TO
DERMATOLOGY, INT
-PR OV EST PT LEV 3
-PR COLONOSCOPY
W/BIOPSY(S)
-PR COLONOSCOPY
W/RMVL
LESN/TUM/POLYP SNARE

hemorrhage) (562.10)
-Family history of
malignant neoplasm of
gastrointestinal tract
(V16.0)
-Hemorrhage of rectum and
anus (569.3)
-Other screening
mammogram (V76.12)
-Symptomatic menopausal
or female climacteric states
(627.2)
-Routine general medical
examination at a health care
facility (V70.0)
-Special screening for
osteoporosis (V82.81)
-Myopia (367.1)
-Hypermetropia (367.0)
-Astigmatism, unspecified
(367.20)
-Other specified visual
disturbances (368.8)

-SIMVASTATIN 20 MG PO
TABS

-REFERRAL TO GI, INT
-GI, INT
-PR COLONOSCOPY DIAG

-FOSAMAX 70 MG PO TABS

-DEXA BONE DENSITY
AXIAL SKELETON
HIPS/PELV/SPINE
-BONE DENSITY (DEXA)
-MAMMO SCREENING
BILATERAL DIGITAL
-DEXA BONE DENSITY
STUDY 1+ SITES AXIAL
SKEL (HIP/PELVIS/SPINE)
-PR OV EST PT LEV 3
-PR REFRACTION
-DIABETIC EYE EXAM (NO
BILL)
-PR VISUAL FIELD EXAM
EXTENDED
-INFLUENZA VAC (FLU
CLINIC ONLY) 3+YO PA
-PR VISUAL FIELD EXAM
LIMITED

-Screening for malignant
neoplasms of cervix
(V76.2)
-Routine general medical
examination at a health care
facility (V70.0)
-Routine gynecological
examination (V72.31)
-Screening for unspecified
condition (V82.9)
-Regular astigmatism
(367.21)
-Astigmatism, unspecified
(367.20)
-Other specified visual
disturbances (368.8)
-Hypermetropia (367.0)

-CHART ABSTRACTION
SIMP (V70.3)
-PR OV EST PT LEV 2
-TDAP VAC 11-64 YO
*ADACEL (V06.8) IM
-GYN CYTOLOGY (PAP) PA
-PR OV EST PT LEV 3

Acute pharyngitis (462)

-Acute sinusitis,
unspecified (461.9)
-Acute nasopharyngitis
[common cold] (460)
-Bronchitis, not specified as
acute or chronic (490)
-Conjunctivitis, unspecified
(372.30)

Screening for unspecified
condition (V82.9)

-Other screening
mammogram (V76.12)
-Other general medical
examination for
administrative purposes
(V70.3)
-Need for prophylactic
vaccination and inoculation
against tetanus-diphtheria
[Td] (DT) (V06.5)
-Screening for malignant
neoplasms of cervix
(V76.2)

-AZITHROMYCIN 250 MG PO
TABS
-AMOXICILLIN 500 MG PO
CAPS
-FLONASE 50 MCG/ACT NA
SUSP
-PROMETHAZINE-CODEINE
6.25-10 MG/5ML PO SYRP
-AUGMENTIN 875-125 MG PO
TABS
-PR OV EST PT LEV 3
-PR OV EST PT LEV 2
-IMMUN ADMIN
IM/SQ/ID/PERC 1ST VAC
ONLY
-MAMMOGRAM SCREENING
-GYN CYTOLOGY (PAP) PA

Disorder of bone and
cartilage, unspecified
(733.90)

Presbyopia (367.4)

Screening for lipoid
disorders (V77.91)

Myopia (367.1)

-GLASSES

-GLASSES

-PR REFRACTION
-CONTACT LENS MISC PA
-INFLUENZA VAC (FLU
CLINIC ONLY) 3+YO PA
-PR VISUAL FIELD EXAM
EXTENDED
-DIABETIC EYE EXAM (NO
BILL)
-PR STREP A RAPID ASSAY
W/OPTIC
-SERV PROV DURING REG
SCHED EVE/WKEND/HOL
HRS
-PULSE OXIMETRY SINGLE
-ENT, INT
-CHART ABSTRACTION
SIMP (V70.3)

Screening for malignant
neoplasms of cervix
(V76.2)

Impotence of organic origin
(607.84)

Unspecified sinusitis
(chronic) (473.9)

Need for prophylactic
vaccination and inoculation
against viral hepatitis
(V05.3)

Senile cataract, unspecified
(366.10)

-Other screening
mammogram (V76.12)
-Screening for lipoid
disorders (V77.91)
-Routine general medical
examination at a health care
facility (V70.0)
-Screening for unspecified
condition (V82.9)
-Hypertrophy (benign) of
prostate without urinary
obstruction and other lower
urinary tract symptom
(LUTS) (600.00)
-Other and unspecified
hyperlipidemia (272.4)
-Hypertrophy (benign) of
prostate with urinary
obstruction and other lower
urinary tract symptoms
(LUTS) (600.01)
-Unspecified essential
hypertension (401.9)
-Acute sinusitis,
unspecified (461.9)
-Allergic rhinitis, cause
unspecified (477.9)
-Dysfunction of Eustachian
tube (381.81)
-Headache (784.0)

-Other specified counseling
(V65.49)
-Need for prophylactic
vaccination and inoculation
against other combinations
of diseases (V06.8)
-Need for prophylactic
vaccination and inoculation
against poliomyelitis
(V04.0)
-Need for prophylactic
vaccination and inoculation
against tetanus-diphtheria
[Td] (DT) (V06.5)
-Presbyopia (367.4)
-Regular astigmatism
(367.21)
-Preglaucoma, unspecified
(365.00)
-Tear film insufficiency,
unspecified (375.15)

-GYN CYTOLOGY (PAP) PA
-OBTAINING SCREEN PAP
SMEAR
-MAMMOGRAM SCREENING
-PR OV EST PT LEV 3
-PR OV EST PT LEV 2

-VIAGRA 100 MG PO TABS
-SILDENAFIL CITRATE 100
MG PO TABS
-VIAGRA 50 MG PO TABS
-TADALAFIL 20 MG PO
TABS
-SIMVASTATIN 20 MG PO
TABS

-PR OV EST PT LEV 3
-IMMUN ADMIN
IM/SQ/ID/PERC 1ST VAC
ONLY
-PR DESTR PRE-MALIG
LESN 1ST
-PR OV EST PT LEV 2
-INFLUENZA VAC 3+YR
(V04.81) IM

-AMOXICILLIN-POT
CLAVULANATE 875-125 MG
PO TABS
-FLUTICASONE
PROPIONATE 50 MCG/ACT
NA SUSP
-MOMETASONE FUROATE
50 MCG/ACT NA SUSP
-FLONASE 50 MCG/ACT NA
SUSP
-AUGMENTIN 875-125 MG PO
TABS
-CIPROFLOXACIN HCL 500
MG PO TABS
-MEFLOQUINE HCL 250 MG
PO TABS

-PR NASAL ENDO DIAG
-ENT, INT
-CT MAXILLOFACIAL W/O
CONTRAST
-PR LARYNGOSCOPY FLEX
DIAG
-REFERRAL TO ENT, INT

-GLASSES

-PR REFRACTION
-PR VISUAL FIELD EXAM
EXTENDED
-DIABETIC EYE EXAM (NO
BILL)
-PR VISUAL FIELD EXAM
LIMITED
-PR OPHTHALMIC DX
IMAGING
-PR INFLUENZA VACC
PRES FREE 3+ YO 0.5ML IM
-MAMMO SCREENING
BILATERAL DIGITAL
-PR OV EST PT LEV 3
-IMMUN ADMIN
IM/SQ/ID/PERC 1ST VAC
ONLY
-TDAP VAC 11-64 YO
*ADACEL (V06.8) IM
-ASP/INJ MAJOR
JOINT/BURS/CYST

Insomnia, unspecified
(780.52)

-Depressive disorder, not
elsewhere classified (311)
-Other malaise and fatigue
(780.79)
-Sleep disturbance,
unspecified (780.50)
-Need for prophylactic
vaccination and inoculation
against influenza (V04.81)

-ZOLPIDEM TARTRATE 10
MG PO TABS
-AMBIEN 10 MG PO TABS
-LORAZEPAM 0.5 MG PO
TABS
-ZOLPIDEM TARTRATE 5
MG PO TABS
-TRAZODONE HCL 50 MG PO
TABS

Osteoarthrosis, localized,
primary, lower leg (715.16)

-Osteoarthrosis, localized,
not specified whether

-HYDROCODONEACETAMINOPHEN 10-325

-HEP A VAC ADULT (V05.3)
IM
-IMMUN ADMIN, EACH
ADD
-HEP B VAC ADULT (V05.3)
IM
-IMMUN ADMIN
IM/SQ/ID/PERC 1ST VAC
ONLY
-TDAP VAC 11-64 YO
*ADACEL (V06.8) IM

Obesity, unspecified
(278.00)

Screening for malignant
neoplasms of prostate
(V76.44)

Unspecified vitamin D
deficiency (268.9)

Lumbago (724.2)

primary or secondary,
lower leg (715.36)
-Chondromalacia of patella
(717.7)
-Pain in joint, pelvic region
and thigh (719.45)
-Enthesopathy of hip region
(726.5)

MG PO TABS
-HYDROCODONEACETAMINOPHEN 5-500 MG
PO TABS

-Routine general medical
examination at a health care
facility (V70.0)
-Other and unspecified
hyperlipidemia (272.4)
-Unspecified sleep apnea
(780.57)
-Unspecified essential
hypertension (401.9)
-Special screening for
malignant neoplasms of
colon (V76.51)
-Screening for unspecified
condition (V82.9)
-Impaired fasting glucose
(790.21)
-Impotence of organic
origin (607.84)
-Laboratory examination
ordered as part of a routine
general medical
examination (V72.62)
-Other malaise and fatigue
(780.79)
-Need for prophylactic
vaccination and inoculation
against diphtheria-tetanuspertussis, combined [DTP]
[DTaP] (V06.1)
-Anemia, unspecified
(285.9)
-Sciatica (724.3)
-Cervicalgia (723.1)
-Thoracic or lumbosacral
neuritis or radiculitis,
unspecified (724.4)
-Degeneration of lumbar or
lumbosacral intervertebral
disc (722.52)

-METFORMIN HCL 500 MG
PO TABS
-HYDROCHLOROTHIAZIDE
25 MG PO TABS

Backache, unspecified
(724.5)

-Sciatica (724.3)
-Cervicalgia (723.1)
-Thoracic or lumbosacral
neuritis or radiculitis,
unspecified (724.4)
-Pain in joint, pelvic region
and thigh (719.45)

Thoracic or lumbosacral
neuritis or radiculitis,
unspecified (724.4)

-Spinal stenosis, lumbar
region, without neurogenic
claudication (724.02)
-Displacement of lumbar
intervertebral disc without
myelopathy (722.10)
-Sciatica (724.3)
-Lumbago (724.2)

-TRIAMCINOLONE
ACETONIDE *KENALOG
INJ
-REFERRAL TO
ORTHOPEDICS, INT
-KNEE 3 VIEW
-BETAMETH ACET 3MG &
NA PHOS 3MG
*CELESTONE INJ
-PR OV EST PT LEV 3
-NUTRITION, INT
-PR ECG AND
INTERPRETATION
-IMMUN ADMIN
IM/SQ/ID/PERC 1ST VAC
ONLY
-PR OV EST PT LEV 2

-SIMVASTATIN 20 MG PO
TABS

-IMMUN ADMIN
IM/SQ/ID/PERC 1ST VAC
ONLY
-PR OV EST PT LEV 3
-PR OV EST PT LEV 2
-GI, INT
-TDAP VAC 11-64 YO
*ADACEL (V06.8) IM

-ERGOCALCIFEROL 50000
UNITS PO CAPS
-FLUTICASONE
PROPIONATE 50 MCG/ACT
NA SUSP
-SIMVASTATIN 20 MG PO
TABS
-OMEPRAZOLE 20 MG PO
CPDR
-ZOLPIDEM TARTRATE 10
MG PO TABS

-MAMMO SCREENING
BILATERAL DIGITAL
-TDAP VAC 11-64 YO
*ADACEL (V06.8) IM
-DEXA BONE DENSITY
AXIAL SKELETON
HIPS/PELV/SPINE
-EKG
-REFERRAL TO GI, INT

-HYDROCODONEACETAMINOPHEN 5-500 MG
PO TABS
-CYCLOBENZAPRINE HCL
10 MG PO TABS
-FLEXERIL 10 MG PO TABS
-VICODIN 5-500 MG PO TABS
-CARISOPRODOL 350 MG PO
TABS
-HYDROCODONEACETAMINOPHEN 5-500 MG
PO TABS
-CYCLOBENZAPRINE HCL
10 MG PO TABS
-NAPROXEN 500 MG PO
TABS
-FLEXERIL 10 MG PO TABS
-VICODIN 5-500 MG PO TABS
-HYDROCODONEACETAMINOPHEN 10-325
MG PO TABS
-HYDROCODONEACETAMINOPHEN 5-500 MG
PO TABS
-GABAPENTIN 300 MG PO
CAPS
-CYCLOBENZAPRINE HCL
10 MG PO TABS
-PREDNISONE 20 MG PO
TABS

-LS SPINE 2 VIEWS
-LUMBAR SPINE MRI W/O
CONTR
-PHYSICAL MEDICINE, INT
-PHYSICAL THERAPY, INT
-PHYSICAL MEDICINE, INT

-LS SPINE 2 VIEWS
-PHYSICAL MEDICINE, INT
-PR ECG AND
INTERPRETATION
-LUMBAR SPINE MRI W/O
CONTR
-PR OV EST PT LEV 3
-LUMBAR SPINE MRI W/O
CONTR
-MRI LUMBAR SPINE WO
CONTRAST
-REFERRAL TO PHYSICAL
MEDICINE, INT
-PHYSICAL MEDICINE, INT
-PHYSICAL MEDICINE, INT

Inflamed seborrheic
keratosis (702.11)

-Other chronic dermatitis
due to solar radiation
(692.74)
-Other dyschromia (709.09)
-Viral warts, unspecified
(078.10)
-Nevus, non-neoplastic
(448.1)

-TRIAMCINOLONE
ACETONIDE 0.1 % EX CREA
-ECONAZOLE NITRATE 1 %
EX CREA

Other seborrheic keratosis
(702.19)

-Actinic keratosis (702.0)
-Inflamed seborrheic
keratosis (702.11)
-Other dyschromia (709.09)
-Personal history of other
malignant neoplasm of skin
(V10.83)

-TRIAMCINOLONE
ACETONIDE 0.1 % EX CREA
-FLUOCINONIDE 0.05 % EX
CREA

Allergic rhinitis due to
other allergen (477.8)

-Allergic rhinitis due to
pollen (477.0)
-Asthma, unspecified type,
unspecified (493.90)
-Extrinsic asthma,
unspecified (493.00)
-Acute sinusitis,
unspecified (461.9)

Allergic rhinitis, cause
unspecified (477.9)

-Acute sinusitis,
unspecified (461.9)
-Routine general medical
examination at a health care
facility (V70.0)
-Acute upper respiratory
infections of unspecified
site (465.9)
-Cough (786.2)

Abdominal pain,
unspecified site (789.00)

-Abdominal pain, epigastric
(789.06)
-Abdominal pain, other
specified site (789.09)
-Esophageal reflux
(530.81)
-Constipation, unspecified
(564.00)

(V72.3)

-Other general medical
examination for
administrative purposes
(V70.3)
-Need for prophylactic
vaccination and inoculation
against tetanus-diphtheria
[Td] (DT) (V06.5)
-Screening for unspecified
condition (V82.9)
-Symptomatic menopausal
or female climacteric states
(627.2)
-Screening for malignant
neoplasms of prostate
(V76.44)
-Routine general medical
examination at a health care

-FLUTICASONE
PROPIONATE 50 MCG/ACT
NA SUSP
-FLONASE 50 MCG/ACT NA
SUSP
-MOMETASONE FUROATE
50 MCG/ACT NA SUSP
-ALLEGRA 180 MG PO TABS
-NASONEX 50 MCG/ACT NA
SUSP
-FLUTICASONE
PROPIONATE 50 MCG/ACT
NA SUSP
-FLONASE 50 MCG/ACT NA
SUSP
-MOMETASONE FUROATE
50 MCG/ACT NA SUSP
-AZITHROMYCIN 250 MG PO
TABS
-NASONEX 50 MCG/ACT NA
SUSP
-CIPRO 500 MG PO TABS
-METRONIDAZOLE 500 MG
PO TABS
-OMEPRAZOLE 20 MG PO
CPDR
-HYDROCODONEACETAMINOPHEN 5-500 MG
PO TABS
-ACIPHEX 20 MG PO TBEC
-ALBUTEROL 90 MCG/ACT
IN AERS

Other specified
vaccinations against
streptococcus pneumoniae
[pneumococcus] (V03.82)

-ALBUTEROL SULFATE HFA
108 (90 BASE) MCG/ACT IN
AERS

-PR DESTRUCT BENIGN
LESION UP TO 14 LESIONS
(LIQUID NITROGEN)
-PR DESTR PRE-MALIG
LESN 1ST
-PR DESTR PRE-MALIG
LESN 2-14
-PR BIOPSY OF
SKIN/SQ/MUC MEMBR
LESN SINGLE
-DERMATOLOGY, INT
-PR DESTR PRE-MALIG
LESN 1ST
-PR DESTR PRE-MALIG
LESN 2-14
-PR BIOPSY OF
SKIN/SQ/MUC MEMBR
LESN SINGLE
-PR DESTRUCT BENIGN
LESION UP TO 14 LESIONS
(LIQUID NITROGEN)
-PR OV EST PT LEV 3
-IMMUNOTHERAPY
MULTIPLE
-ANTIGEN ALLERGY MAT
FOR INJ (06041) PA
-PR IMMUNOTX W
EXTRACT 2+ INJ
-INFLUENZA VAC (FLU
CLINIC ONLY) 3+YO PA
-ENT, INT
-PR OV EST PT LEV 3
-IMMUN ADMIN
IM/SQ/ID/PERC 1ST VAC
ONLY
-IMMUNOTHERAPY
MULTIPLE
-MAMMOGRAM
SCREENING
-INFLUENZA VAC (FLU
CLINIC ONLY) 3+YO PA
-US ABDOMEN COMPLETE
-GI, INT
-ABDOMEN/PELVIS CT PA
-PR ECG AND
INTERPRETATION
-SERV PROV DURING REG
SCHED EVE/WKEND/HOL
HRS
-GYN CYTOLOGY (PAP) PA
-MAMMOGRAM
SCREENING
-CHART ABSTRACTION
SIMP (V70.3)
-PR OV EST PT LEV 2
-TD VAC 7+ YO (V06.5) IM

-PNEUMOCOCCAL VAC
(V03.82) IM/SQ
-PNEUMOCOCCAL
POLYSACC VAC 2+ YO
(V03.82) IM/SQ

Other psoriasis (696.1)

facility (V70.0)
-Need for prophylactic
vaccination and inoculation
against tetanus-diphtheria
[Td] (DT) (V06.5)
-Need for prophylactic
vaccination and inoculation
against other specified
disease (V05.8)
-Other dyschromia (709.09)
-Other atopic dermatitis
and related conditions
(691.8)
-Seborrheic dermatitis,
unspecified (690.10)
-Unspecified pruritic
disorder (698.9)

Other general medical
examination for
administrative purposes
(V70.3)

-Routine general medical
examination at a health care
facility (V70.0)
-Need for prophylactic
vaccination and inoculation
against tetanus-diphtheria
[Td] (DT) (V06.5)
-Screening for lipoid
disorders (V77.91)

Migraine, unspecified,
without mention of
intractable migraine
without mention of status
migrainosus (346.90)

-Anxiety state, unspecified
(300.00)
-Cervicalgia (723.1)
-Other screening
mammogram (V76.12)
-Myalgia and myositis,
unspecified (729.1)

Routine general medical
examination at a health care
facility (V70.0)

-Other screening
mammogram (V76.12)
-Special screening for
malignant neoplasms of
colon (V76.51)
-Screening for malignant
neoplasms of prostate
(V76.44)
-Need for prophylactic
vaccination and inoculation
against diphtheria-tetanuspertussis, combined [DTP]
[DTaP] (V06.1)
-Routine general medical
examination at a health care
facility (V70.0)
-Benign neoplasm of colon
(211.3)
-Screening for unspecified
condition (V82.9)
-Other screening
mammogram (V76.12)
-Pain in joint, shoulder
region (719.41)
-Pain in joint, lower leg
(719.46)
-Lumbago (724.2)
-Pain in joint, pelvic region
and thigh (719.45)

Special screening for
malignant neoplasms of
colon (V76.51)

Care involving other
physical therapy (V57.1)

-IMMUN ADMIN
IM/SQ/ID/PERC 1ST VAC
ONLY
-INFLUENZA VAC 3+YR
(V04.81) IM
-TDAP VAC 11-64 YO
*ADACEL (V06.8) IM
-CLOBETASOL PROPIONATE
0.05 % EX OINT
-CLOBETASOL PROPIONATE
0.05 % EX CREA
-FLUOCINONIDE 0.05 % EX
CREA
-DESONIDE 0.05 % EX CREA
-TRIAMCINOLONE
ACETONIDE 0.1 % EX CREA
-ALBUTEROL 90 MCG/ACT
IN AERS
-FLONASE 50 MCG/ACT NA
SUSP

-SUMATRIPTAN SUCCINATE
50 MG PO TABS
-HYDROCODONEACETAMINOPHEN 5-500 MG
PO TABS
-PROMETHAZINE HCL 25
MG PO TABS
-IMITREX 100 MG PO TABS
-ZOLPIDEM TARTRATE 10
MG PO TABS
-PR OV EST PT LEV 3
-PR OV EST PT LEV 2
-IMMUN ADMIN
IM/SQ/ID/PERC 1ST VAC
ONLY
-TDAP VAC 11-64 YO
*ADACEL (V06.8) IM
-GYN CYTOLOGY (PAP) PA

-PR ULTRAVIOLET
THERAPY
-DERMATOLOGY, INT
-PR BIOPSY OF
SKIN/SQ/MUC MEMBR
LESN SINGLE
-PR DESTR PRE-MALIG
LESN 1ST
-DERMATOLOGY, INT
-CHART ABSTRACTION
SIMP (V70.3)
-CHART ABSTRACTION
INT (V70.3)
-PAMFONLINE
ENROLLMENT
-MAMMOGRAM
SCREENING
-PR CHART ABSTRACTION
COMP (V70.3)
-MAMMO SCREENING
BILATERAL DIGITAL
-GYN CYTOLOGY (PAP) PA
-PR OV EST PT LEV 3
-MAMMOGRAM
SCREENING
-NEUROLOGY, INT

-PEG-KCL-NACL-NASULFNA ASC-C 100 G PO SOLR

-GI, INT
-REFERRAL TO GI, INT
-PR OV EST PT LEV 3
-PR OV EST PT LEV 2
-IMMUN ADMIN
IM/SQ/ID/PERC 1ST VAC
ONLY

-HYDROCODONEACETAMINOPHEN 5-500 MG
PO TABS
-HYDROCODONEACETAMINOPHEN 10-325
MG PO TABS
-CYCLOBENZAPRINE HCL
10 MG PO TABS
-HYDROCODONE-

-PR PHYS THERAPY
EVALUATION
-PR THERAPEUTIC
EXERCISES
-PR MANUAL THER TECH
1+REGIONS EA 15 MIN
-PR ELECTRIC
STIMULATION THERAPY
-REFERRAL TO PHYSICAL
THERAPY, INT

ACETAMINOPHEN 5-325 MG
PO TABS
-FOLIC ACID 1 MG PO TABS
-METHOTREXATE 2.5 MG PO
TABS
-PREDNISONE 5 MG PO
TABS
-PREDNISONE 1 MG PO
TABS
-HYDROXYCHLOROQUINE
SULFATE 200 MG PO TABS

Rheumatoid arthritis
(714.0)

-Unspecified inflammatory
polyarthropathy (714.9)
-Arthropathy, unspecified,
site unspecified (716.90)
-Sicca syndrome (710.2)

Osteoporosis, unspecified
(733.00)

-Unspecified acquired
hypothyroidism (244.9)
-Osteoarthrosis, unspecified
whether generalized or
localized, site unspecified
(715.90)
-Benign essential
hypertension (401.1)
-Unspecified essential
hypertension (401.9)

-FOSAMAX 70 MG PO TABS
-ALENDRONATE SODIUM 70
MG PO TABS
-HYDROCHLOROTHIAZIDE
25 MG PO TABS
-ACTONEL 35 MG PO TABS

Other malaise and fatigue
(780.79)

-Routine general medical
examination at a health care
facility (V70.0)
-Dizziness and giddiness
(780.4)
-Other respiratory
abnormalities (786.09)
-Screening for unspecified
condition (V82.9)
-Atrial fibrillation (427.31)
-Congestive heart failure,
unspecified (428.0)
-Coronary atherosclerosis
of unspecified type of
vessel, native or graft
(414.00)

-AZITHROMYCIN 250 MG PO
TABS
-FLUTICASONE
PROPIONATE 50 MCG/ACT
NA SUSP

Long-term (current) use of
anticoagulants (V58.61)

Spinal stenosis, lumbar
region, without neurogenic
claudication (724.02)

-Degeneration of lumbar or
lumbosacral intervertebral
disc (722.52)
-Lumbosacral spondylosis
without myelopathy (721.3)
-Displacement of lumbar
intervertebral disc without
myelopathy (722.10)
-Lumbago (724.2)

Unspecified hemorrhoids
without mention of
complication (455.6)

-Internal hemorrhoids
without mention of
complication (455.0)
-Benign neoplasm of colon
(211.3)
-Anal or rectal pain
(569.42)
-Personal history of colonic
polyps (V12.72)
-Dermatophytosis of foot
(110.4)
-Ingrowing nail (703.0)
-Keratoderma, acquired
(701.1)
-Peripheral vascular
disease, unspecified (443.9)

Dermatophytosis of nail
(110.1)

-RHEUMATOLOGY, INT
-METHYLPRED ACET
80MG *DEPO-MEDROL INJ
-TRIAMCINOLONE
ACETONIDE *KENALOG
INJ
-ASP/INJ INT
JOINT/BURS/CYST
-PR METHLYPRED ACET
(BU 21 - 40MG) *DEPOMEDROL INJ
-DEXA BONE DENSITY
AXIAL SKELETON
HIPS/PELV/SPINE
-BONE DENSITY (DEXA)
-DEXA BONE DENSITY
STUDY 1+ SITES AXIAL
SKEL (HIP/PELVIS/SPINE)
-INFLUENZA VAC 3+YR
(V04.81) IM
-MAMMOGRAM
SCREENING
-PR ECG AND
INTERPRETATION
-CHEST PA & LATERAL
-PR OV EST PT LEV 3
-EKG
-TDAP VAC 11-64 YO
*ADACEL (V06.8) IM

-WARFARIN SODIUM 5 MG
PO TABS
-COUMADIN 5 MG PO TABS
-WARFARIN SODIUM 2.5 MG
PO TABS
-ATENOLOL 50 MG PO TABS
-WARFARIN SODIUM 2 MG
PO TABS
-HYDROCODONEACETAMINOPHEN 10-325
MG PO TABS
-GABAPENTIN 300 MG PO
CAPS
-HYDROCODONEACETAMINOPHEN 5-500 MG
PO TABS
-TRAMADOL HCL 50 MG PO
TABS
-PREDNISONE 20 MG PO
TABS
-HYDROCORTISONE
ACETATE 25 MG PR SUPP
-ANUSOL-HC 25 MG PR SUPP
-HYDROCORTISONE 2.5 %
PR CREA
-ANUSOL-HC 2.5 % PR CREA
-PEG-KCL-NACL-NASULFNA ASC-C 100 G PO SOLR

-PR OV EST PT MIN SERV
-PR PROTHROMBIN TIME
-DEBRIDEMENT OF NAILS,
6 OR MORE
-PR ECG AND
INTERPRETATION
-EKG

-ECONAZOLE NITRATE 1 %
EX CREA
-TERBINAFINE HCL 250 MG
PO TABS
-TRIAMCINOLONE
ACETONIDE 0.1 % EX CREA

-DEBRIDEMENT OF NAILS,
6 OR MORE
-TRIM SKIN LESIONS, 2 TO
4
-PARE CORN/CALLUS,
SINGLE LESION
-PODIATRY, INT

-LUMBAR SPINE MRI W/O
CONTR
-LS SPINE 2 VIEWS
-PHYSICAL MEDICINE, INT
-MRI LUMBAR SPINE WO
CONTRAST
-PHYSICAL MEDICINE, INT

-ANOSCOPY
-SURGERY, INT
-PR COLONOSCOPY
W/BIOPSY(S)
-LIGATION OF
HEMORRHOID(S)
-GI, INT

-CEPHALEXIN 500 MG PO
CAPS
-HYDROCODONEACETAMINOPHEN 5-500 MG
PO TABS
-VICODIN 5-500 MG PO TABS
-IBUPROFEN 600 MG PO
TABS

Pain in joint, lower leg
(719.46)

-Chondromalacia of patella
(717.7)
-Tear of medial cartilage or
meniscus of knee, current
(836.0)
-Pain in limb (729.5)
-Pain in joint, pelvic region
and thigh (719.45)

Pain in joint, pelvic region
and thigh (719.45)

-Lumbago (724.2)
-Sciatica (724.3)
-Backache, unspecified
(724.5)
-Pain in joint, lower leg
(719.46)

-HYDROCODONEACETAMINOPHEN 5-500 MG
PO TABS
-VICODIN 5-500 MG PO TABS

Anxiety state, unspecified
(300.00)

-Insomnia, unspecified
(780.52)
-Generalized anxiety
disorder (300.02)
-Other malaise and fatigue
(780.79)
-Headache (784.0)

Generalized anxiety
disorder (300.02)

-Depressive disorder, not
elsewhere classified (311)
-Insomnia, unspecified
(780.52)
-Irritable bowel syndrome
(564.1)
-Sleep disturbance,
unspecified (780.50)

Cervicalgia (723.1)

-Lumbago (724.2)
-Backache, unspecified
(724.5)
-Headache (784.0)
-Brachial neuritis or
radiculitis NOS (723.4)

Impaired fasting glucose
(790.21)

-Routine general medical
examination at a health care
facility (V70.0)
-Screening for malignant
neoplasms of prostate
(V76.44)
-Other and unspecified
hyperlipidemia (272.4)
-Overweight (278.02)
-Other chronic dermatitis
due to solar radiation
(692.74)
-Other seborrheic keratosis
(702.19)
-Benign neoplasm of other
specified sites of skin
(216.8)
-Neoplasm of uncertain
behavior of skin (238.2)

-LORAZEPAM 0.5 MG PO
TABS
-ZOLPIDEM TARTRATE 10
MG PO TABS
-LORAZEPAM 1 MG PO
TABS
-ALPRAZOLAM 0.25 MG PO
TABS
-CLONAZEPAM 0.5 MG PO
TABS
-LORAZEPAM 0.5 MG PO
TABS
-CLONAZEPAM 0.5 MG PO
TABS
-TRAZODONE HCL 50 MG PO
TABS
-LORAZEPAM 1 MG PO
TABS
-AMBIEN 10 MG PO TABS
-CYCLOBENZAPRINE HCL
10 MG PO TABS
-HYDROCODONEACETAMINOPHEN 5-500 MG
PO TABS
-FLEXERIL 10 MG PO TABS
-NAPROXEN 500 MG PO
TABS
-CARISOPRODOL 350 MG PO
TABS
-SIMVASTATIN 20 MG PO
TABS
-HYDROCHLOROTHIAZIDE
25 MG PO TABS
-SIMVASTATIN 40 MG PO
TABS

Actinic keratosis (702.0)

-TRIAMCINOLONE
ACETONIDE 0.1 % EX CREA
-FLUOCINONIDE 0.05 % EX
CREA
-GLASSES

-PR DESTR PRE-MALIG
LESN 1ST
-ASP/INJ MAJOR
JOINT/BURS/CYST
-REFERRAL TO
ORTHOPEDICS, INT
-TRIAMCINOLONE
ACETONIDE *KENALOG
INJ
-LOWER EXTREMITY
JOINT MRI W/O CONTR
-ORTHOPEDICS, INT
-PELVIS LIMITED
-LS SPINE 2 VIEWS
-XR PELVIS 1 OR 2 VIEWS
-REFERRAL TO
ORTHOPEDICS, INT
-ASP/INJ MAJOR
JOINT/BURS/CYST
-MAMMO SCREENING
BILATERAL DIGITAL
-EKG
-PR ECG AND
INTERPRETATION
-PR INFLUENZA VACC
PRES FREE 3+ YO 0.5ML IM
-TDAP VAC 11-64 YO
*ADACEL (V06.8) IM
-MAMMOGRAM
SCREENING
-GI, INT
-CHART ABSTRACTION
INT (V70.3)

-PHYSICAL MEDICINE, INT
-SPINE CERVICAL
COMPLETE
-PHYSICAL THERAPY, INT
-PHYSICAL THERAPY, EXT
-PR THERAPEUTIC
EXERCISES

-PR OV EST PT LEV 3
-TDAP VAC 11-64 YO
*ADACEL (V06.8) IM
-PR OV EST PT LEV 2
-NUTRITION, INT
-PR INFLUENZA VACC
PRES FREE 3+ YO 0.5ML IM
-PR DESTR PRE-MALIG
LESN 1ST
-PR DESTR PRE-MALIG
LESN 2-14
-PR BIOPSY OF
SKIN/SQ/MUC MEMBR
LESN SINGLE
-SURGICAL PATHOLOGY
PA
-PR OV EST PT LEV 3

