TeamHADOC_PRED498_SEC59_FA2015_PP_FinalPaper

Running head: REDUCING HEALTHCARE READMISSIONS 1
Reducing Healthcare Readmissions and
Fostering Value for Healthcare Payers
By
Stephanie Couch
Jonathan Frye
Jason Jobes
Capstone Project
Submitted in partial fulfillment of the
Requirements for the degree of
MASTER OF SCIENCE IN PREDICTIVE ANALYTICS
December 2015
Faculty Advisor: Nethra Sambamoorthi, PhD

REDUCING HEALTHCARE READMISSIONS 2
Team Roles and Responsibilities
Team Members Role Responsibility
Stephanie Couch Research, Document Prep Ensure completion of project, prepare all
documents for final submission, and make
sure data and models make sense.
Jonathan Frye Senior Analyst, R
Programmer
Create models, develop interactive
dashboard, collect data, analyze data, and
validate results. Contribute major project
model deliverables.
Jason Jobes Team Communication,
Team Lead, Document
Prep
Ensure completion of project, ensure
efficient collection and validity of data,
analyze data, validate data, and keep client
and advisor communications through
weekly emails and telephone calls.

Table of Contents
Team Roles and Responsibilities.....................................................................................................2
Abstract............................................................................................................................................5
Introduction......................................................................................................................................6
The Advisory Board in Brief ...............................................................................................6
Problem Statement...........................................................................................................................7
Problem Description ............................................................................................................7
Solution Objective ...............................................................................................................9
Business Case.................................................................................................................................10
Literature Review...........................................................................................................................12
Methods..........................................................................................................................................13
Model Description .............................................................................................................15
Ranking Model for Provider Value....................................................................................16
Decision Tree Models........................................................................................................16
Linear Models Validate and Extend Research...................................................................21
Regression Model Variable Importance Using Random Forest ........................................22
Model Results ................................................................................................................................28
Best Model Select ..............................................................................................................28
Ranking Healthcare Providers ...........................................................................................29
Predicting Healthcare Provider Value ...............................................................................30
Predicting Reduced Risked-based Cost Per Patient...........................................................31
Implementation of the Model.........................................................................................................32
Demonstration....................................................................................................................33

Use Case.............................................................................................................................37
Challenges......................................................................................................................................38
Opportunities..................................................................................................................................40
Recommendations..........................................................................................................................41
Project Review...............................................................................................................................42
Conclusion .....................................................................................................................................45
References......................................................................................................................................46
Appendix A....................................................................................................................................49
Appendix B....................................................................................................................................59
Appendix C....................................................................................................................................66
Appendix D....................................................................................................................................71
Solution Development and Integration Report ..............................................................................77

Abstract
The Department of Health and Human Services is pushing aggressively to change the incentive
and reimbursement structure in healthcare from one that rewards volume to one that rewards
value. A key component of this structure is to decrease readmissions to acute care facilities
within 30 days of an initial discharge. Currently, while data is available to payers and consumers,
it is often not aggregated or provided in a way that helps informed consumer choices. In this
project, Healthcare Analytics Data Operations Consultants, with stakeholders of The Advisory
Board Company and Northwestern University, presents an algorithm and interactive dashboard
that is consumer facing and will be used inflect change in utilization habits of patients, ultimately
seeking to drive consumers to the highest quality, lowest cost provider. A ranking model sorts
providers on a quadrant according to a value score by readmissions rates and healthcare costs.
Accordingly, providers are categorized from Best to Worst. A Multivariate Linear Model with
ℛ2
of 0.54 predicts the value of healthcare providers. This model is used to provide prescriptive
recommendations for each provider in five variables that account for 54% of the difference in
value of providers. Payers and consumers of healthcare can measure success by helping affiliated
hospitals improve performance or by showing shifts in utilization trends to higher quality
facilities. Finally, a Univariate Linear Model with 𝑅2
of 0.35 is presented that predicts a reduced
risk-based cost per patient of 1.2% for every 1% increase in the value score used to rank
healthcare providers. This makes a very clear business case for the use of the models by
insurance companies, healthcare management organizations, and consumers.
Keywords: healthcare value, healthcare analytics, predictive modeling, data science

Reducing Healthcare Readmissions and
Fostering Value for Healthcare Payers
Improving quality and decreasing costs in healthcare is a focus for The Advisory Board
Company (ABC). Through its breadth of services and consumer base, ABC seeks to elevate
healthcare performance not only in the United States but globally. However, while improvements
are being made, the United States still ranks near the bottom in overall value of healthcare. The
tide may be shifting here as a perfect storm of regulatory policies is shifting more ownership to
patients seeking medical care. This shift also comes with opportunities to help the consumer or
the provider differentiate high quality facilities. The goal of the HADOC team is to partner with
The Advisory Board Company to create a platform by which healthcare facilities are judged on
their quality and value. This platform will seek to differentiate the providers in the market by
examining and predicting quality and value based on both past performance as well as
organizational characteristics of the facility. By creating a platform that increases transparency to
consumers and payers, ABC will help drive improved quality for patients, reduced cost for
consumers and payers, and elevate healthcare quality in the United States.
The Advisory Board in Brief
The Advisory Board Company is a publicly held research and consulting firm located in
Washington, DC. The firm was originally founded by David Bradley as the Research Counsel of
Washington in 1979. The goal at that time was to answer any question for any company for any
industry. In 1986, the organization launched its first membership division titled the Healthcare
Advisory Board. This membership allowed organizations to have readily available access to
research and information, as well as have dedicated personnel available for custom research.
Since then, the firm has continued to grow to address the complex nature of healthcare through a

series of different offerings. The organization has four verticals—research and insights, talent
development, performance technologies, and consulting and management. This blend of verticals
and business offerings makes leveraging predictive analytics to help drive improved performance
in the healthcare industry an attractive endeavor. Through understanding and leveraging data in
an effort to inflect quality change, ABC can set in place a partnership that will transcend all of its
verticals while serving its needs of its members. If successful, this will prove to be a new service
offering located in the Revenue Cycle Solutions Consulting and Management Division.
Problem Statement
The current problem is that a disproportionate amount of money is spent by healthcare
payers for the quality of care being provided and received in the United States. Care is highly
variable as seen by outcomes in similar geographic locations as seen below in the project
justification section. Ultimately, there is a wave of transparency of information in healthcare yet
few consumers and payers know how to leverage this to improve performance or healthcare
efficiency.
The question is whether a mechanism and model can be created that can help stratify
hospital performance such that quality (healthcare readmissions) and cost can be viewed by
consumers to help inflect change in the healthcare industry.
Problem Description
Healthcare quality is often a challenging term to define. The figure below shows a
comparison of quality for 11 developed countries. It is clear that the United States lags behind
other countries in performance. But, ultimately healthcare quality can be summed up as the
overall value proposition for the payer or the consumer when they are seeking care. Typical
quality measures include access to care and healthcare outcomes. More and more organizations

are investing more to measure and improve outcomes such as mortality, readmissions,
complications, and hospital acquired conditions. This push towards value has also been
encouraged through CMS’s Pay for Performance Program. That program has three distinct
components. They are the Hospital Readmission Reduction Program (HRRP), the Value Based
Purchasing Program (VBP), and the Hospital Acquired Conditions (HAC) program. In fiscal
federal year 2016, 6% of Medicare inpatient payments to hospitals will be at risk based purely on
hospital performance. Of that 6%, half of those dollars at risk are for hospital readmission
performance.
Hospital readmissions constitute a large portion of the penalties because CMS has
identified this as one of the largest single expenditures in the healthcare system. Currently,
facilities are paid for every inpatient admission. Therefore there is a financial incentive to an
organization to provide less than perfect quality because those readmissions can drive increased
revenue. In fact, a 2014 study by the Agency for Healthcare Quality and Research shows that in
2011 there were 3.3 million adult readmissions within 30 days of an initial patient discharge.
These readmissions were associated with 41.3 billion dollars in hospital costs (Hines et al.,
2014). However, there is significant variation in individual facilities and their outcome quality.
While strides have been made to increase the transparency of hospital performance,
consumers are still left to search through a maze of data to uncover and interpret healthcare
quality. However, ABC seeks to improve this visibility into performance by creating a platform
that can be shared with consumers and payers that quantifies overall performance into a single
score. This score would then allow utilizers of healthcare services the ability to select the most
appropriate facility for them and ultimately drive higher quality or more appropriate utilization.

Figure 1: Country Rankings (The Commonwealth Fund, 2014).
Solution Objective
In addition to the business case and perspective noted previously, Team HADOC has
three key objectives for this project.
1. Inform healthcare payer leaders of the disparity between hospitals in geographic areas
related to healthcare quality and cost.
2. Foster partnership between healthcare payers and hospitals to both improve overall
performance as well as increase delivery quality and efficiency.
3. Empower consumers with the ability to identify and ultimately select high quality
healthcare organizations
These objectives center on the partnership with The Advisory Board, the corporate sponsor,
and help to improve healthcare at the payer, provider, and consumer perspective. This is an
interesting dynamic as the model and its outputs will be more consumer facing and the firm itself
is more hospital facing. There is a concern that pushback may be received from member

hospitals but it is imperative that they recognize, embrace, and increase awareness of the
transparency of information early to effectively make change.
Business Case
A way to quantify the cost for readmissions is follows: Based on the Statistical Brief
#172 from the Healthcare Cost and Utilization project, the costs for readmissions would be
$13,049.71 (Hines et al., 2014). Most directly related costs were used in the calculations for
consistency with the decease groups used this research. Once the cost for readmissions is
established, a risk based cost per patient can be calculated. An example is illustrated below:
Tallahassee, FL, is home to two hospitals. Tallahassee Memorial Hospital (TMH) is a Good
Care-Focused Provider, while Capital Regional Medical Center (CRMC) is a Worst Sub-
Performer. See the below table, which outlines the number of Billings, Patients, Readmissions,
and Overall Spending.
TMH CRMC
Billings: 5,761 3,489
Patients: 4,157 2,287
Readmissions: 14.4% 17.7%
Overall Spending: $20,352 $18,385
Risk-based Cost: $30,809 $31,572
Table 1: Comparison of Two Facilities in the Same Market
The Risk-based Cost per Patient (ℛ 𝒾) is calculated as:
ℛ 𝒾 =
ℬ𝒾 𝒪𝒾 + ℬ𝒾ℛ 𝒾 𝒞
𝒫𝒾
Where ℬ𝒾 is the number of billings, 𝒪𝒾 is the overall spending, ℛ 𝒾 is the readmissions rate, 𝒞 is
the cost of readmissions, and 𝒫𝒾 is the number of patients.
Total CMS spending would be $117,247,872 for TMH and $64,145,265 at CRMC. TMH
thus has a total readmissions spending of $10,825,831, and CRMC has a total readmissions
spending of $8,058,888. The risk-based cost per patient at TMH is $30,809 and the risk-based

cost per patient at CRMC is $31,572. So while initially CRMC would be $1,967 cheaper per
visit, a risk-based cost calculation per patient, as demonstrated above, is actually $763 more per
patient at CRMC then at TMH. See below screenshots of TMH and CRMC from the Dashboard
created by Team HADOC.
Figure 2: Tallahassee Memorial Hospital Rank and Information

Figure 3: Capital Regional Medical Center Rank and Information
Literature Review
In regards to the topic of readmissions in the healthcare industry, there have been
numerous studies already conducted. The reason team HADOC has deciding to research
readmissions from the buyer/consumer perspective is to add to the current research in a way that
has not yet been conducted.
One study of interest is entitled “Socioeconomic Status and Readmissions: Evidence
From An Urban Teaching Hospital” (Nerenz, Gonsahn, & Hu, 2014). Data from an urban
teaching hospital was analyzed to examine how certain elements of individual characteristics and
neighborhood socioeconomic status influenced readmissions to occur under a fixed

organizational and staffing structure. In this study, it was found that patients from poorer
neighborhoods and those patients without social and/or family support were more likely to be
readmitted. This lead Team HADOC to the research question at hand: Does this prove that those
patients that can pay for better healthcare are less likely to be readmitted? Should the people who
have the ability to pay for better healthcare indeed pay for better healthcare, and additionally,
should government and insurance companies pay for better healthcare?
Another interesting article related to our research, entitled “Why Paying for Value May
Hurt the Hospitals That Need The Most Help” (Shah, 2015), released by the Advisory Board
Company, illustrates why the current value based payment programs may indeed be a poor fit for
certain hospitals. Some intrinsic qualities, or qualities inherent to the nature of safety-net
hospitals can result in care that is surgically related that is more expensive and has less quality
that providers which are not labeled “safety-net”. 231 hospitals were used in the study, and were
labeled either “low”, “medium”, or “high” safety net burden. Hospital resources were
determined, not individual patient resources, to contribute the most to the outcomes of the study.
Value-Based Reimbursement is at the heart of our study. In the article “The Key to
Transitioning from Fee-for-Service to Value-Based Reimbursement” (Brown and Crapo, 2014),
a study was done on the performance measurement of tracking 30-day readmissions. As noted
earlier, Medicare requires hospitals to track their 30-day readmissions rates. In the future, 90 day
readmission rates will be required to be tracked. This can become a very overwhelming process
incredibly fast. Transitioning to Value-Based Reimbursements will hurt hospitals in the short-
run, because the revenue transition period will need to take place. However as time progresses,
the revenue mix associated with Value-Based Reimbursement will increase, however no one

really knows how long this process will take. Our model proposes a way to begin to capture this
Value.
Methods
Team HADOC ultimately wanted to predict where a particular patient should go for care.
Ultimately utilizing readmissions as a quality driver and the cost of care, we intend for our model
to steer patients in the direction to seek better health outcomes at lower costs. A score to rank
providers was generated for two areas, which include cost of care and rate of readmissions. An
IQ-like scoring method was then utilized to categorize providers in a quadrant which included:
 Low readmissions, Low Cost-Leaders
 Low Readmissions, High Cost Care-Focused
 High Readmissions, Low Cost Cost-Focused
 High Readmissions, High Cost Sub-Performers
From our analysis, we have determined most of the providers will be closely grouped in
the middle of this resulting quadrant, just askew in the direction of one of the four categories. A
z-score was calculated and converted to an IQ-like score. This is explained in further detail in the
“Methodology Visuals Explaining Concepts” section of this study. Two types of models were
used in this context. Linear Models were utilized, which included Decision Tree models, and an
Ensemble Model was utilized, which was comprised of a Random Forest model. The models
were generated to understand why providers rank as they do, by allowing us to analyze why
certain variables contribute to this ranking. A Decision Tree model was created to see why Low
Readmission/Low Cost providers become Leaders. The outcomes could lead to different
strategies depending on the consumer: from the perspective of a Medicare/Medicaid regulator,
the model will show how to structure regulations to get more Leaders; from the perspective of an
insurance company or HMO, the model will show to how work with the providers to get more
hospitals labeled as leaders in the network of providers. Priority would be given to those

facilities which were labeled “Leaders” (low readmissions, low cost). In the ranking model,
exhibited by the Quadrant, an ad hoc analytic metric was used to rank the providers, then the
score was reversed using a negative calculation with the standard deviation in the equation, so
that a higher score is positive, which makes more sense intuitively, as a higher score is generally
interpreted to be better.
Outliers were investigated in the data. Data Quality is outlined in the Solution
Development and Implementation Report, and outlines why only 3,444 providers are listed in the
Merged File, whereas the IPPS Cost Data has data on 3,987 providers, based on Provider ID
numbers. Our modeling objective was to get the best predictive model that utilizes and reduces
the number of variables used in the Merged Data File. The models created for this research were
built from 3,080 observations as subset from the Merged Data File for complete observations.
The 364 observations were excluded from the analysis for missing variables. That still allowed
89.4% of observations to be included in the research.
Three types of models were utilized in this research: a Ranking Model, which created the
basis for the Healthcare Provider Value Quadrant (HPVQ); Regression Models, which include
Univariate and Multivariate Linear Models as well as Ensemble Learning Methods with a
Random Forest; and Classification Models, which include Decision Trees. The final solution
incorporates the Ranking Model, a Univariate Model, and a Multivariate Model with variables
identified in a Decision Tree and Random Forest.
Model Description
A total of nine models were created as part of this research project. The final solution
integrates three of these models with two additional models contributing to the selected variables
used in the Multivariate Linear Model.

Ranking Model for Provider Value
Please see page 92 of this paper, in the Final SDAIR and Project Plan section, under the
subtitle “Methodology Visuals Explaining Concepts, Developed for Your Context”. This section
offers a detailed explanation of how the Ranking Model for Provider Value was created.
Decision Tree Models
These explanatory/predictive models includes attributes which providers cannot change
themselves. An example would be teaching status, in which a hospital is defined as a teaching
hospital if it has accredited residency programs. Major and minor teaching facilities exist
nationwide, as well as non-teaching facilities. A major teaching facility is a facility that is a
member of the Council of Teaching Hospitals (COTH), while minor teaching facilities are not
members of the Council of Teaching Hospitals, but have at least one intern and one resident,
according to Hospitals Today.
Model A
This model appears to be our simplest, yet most meaningful model thus far. Our
dependent variable to be predicted was whether a facility was a leader, with yes or no binary
outcome variables. The independent variables used in the model include factor(URGEO),
Beds.Factor, and Teaching.Status.Factor. The Beds.Factor variable has been split up into 3
categories of factored number of beds. A number less than or equal to 100 is considered “Small”,
a number between 101 and 250 is considered “Medium”, and a number greater than 250 is

considered “Large”. See the below resulting Decision Tree Diagram.
Figure 4: Decision Tree from Model A.
The accuracy on this model is equal to 0.7787611. Accuracy is determined from the
confusion matrix, and is the most widely used metric for performance evaluation. Accuracy is
calculated by summing the True Positives and True Negatives, and dividing this number by the
overall sum of the True Positives, True Negatives, False Positives, and False Negatives.
The Precision of this model is equal to 0.5326633. Precision is the value of the True
Positives, divided by the sum of the True Positives and False Positives. The Recall of this model

is equal to 0.4453782. Recall is the value of the True Positives, divided by the sum of the True
Positives and False Negatives. The F-measure value of this model is equal to 0.4851259. The F-
measure is equal to 2*True Positives divided by the sum of 2*True Positives plus False
Negatives plus False Positives.
Model B
Ownership.Type has been added to this model, which is still predicting whether a facility
will be a leader (Yes or No) as the dependent variable. The independent variables in this tree
now include Ownership.Type, factor(URGEO), Beds.Factor, and Teaching.Status.Factor. See the
below resulting Decision Tree Diagram:
Figure 5: Decision Tree from Model B.
Accuracy for this model was equal to 0.8554572. Precision was equal to 0.4857143.
Recall was equal to 0.1164384. The F-measure was equal to 0.1878453. Accuracy in this model
is greater than Accuracy in model A, however Precision, Recall, and the F-Measure all had much
lower scores in model B than in model A.

An explanation for this drop in Precision, Recall, and F1 could possibly be explained by
exceptions: those facilities which are leaders, but are not small, not non-teaching, or not rural.
Model C
Since the focus of research what on foster value in healthcare through greater quality and
lower readmissions, further models excluded attributes which providers could not change and
focused on numerical variables which could be used as key performance indicators. See below
for a screenshot of a Decision Tree in which all variables in the data set were utilized to predict
the binary class variable, if a facility was a Leader (Yes or No).
Figure 6: Original Decision Tree before Pruning from Model C.
This pre-pruned model is complicated and overwhelming. After pruning, the model is not
only much simpler, but also more useful in the sense that it details four cost areas that providers
need to keep less than the amount listed.

Model C has an Accuracy of 0.8298918, a Precision of 0.3856209, a Recall of
0.4275362, and an F1 measure of 0.4054983. This model exhibits better accuracy then model A,
better Precision then model B, better Recall then model B, and a better F1 measure then model
B. See below for the Decision Tree after pruning:
Figure 7: Pruned Decision Tree from Model C.
The four costs that are identified in this decision tree could be used as Key Performance
Indicators (KPIs) for providers. Post.Discharge.Inpatient and During.Hospitalization.Carrier are
moderately negatively correlated with the Standard.Overall.Value. See the below correlations
plot.

Figure 8: Correlation Plot with the Variables from Model C.
Linear Models to Validate and Further Extend Research
Model D
In a linear model, the four costs illustrated above (During.Hospitalization.Carrier,
Post.Discharge.Inpatient, Post.Discharge.Skilled.Nursing.Facility, and Post.Discharge.Hospice)
are all statistically significant in explaining the Standard.Overall.Value (exhibited in this linear
model).
The adjusted R-squared in this model is 0.4786. That is somewhat low, and can be
attributed to the fact that quality of care, patient attributes, and a lot of other factors would have

to be included in this model as explanatory variables, and are variables that we do not currently
have. However, for a practical model, that is actually very good in the real world.
Model E
In another linear model, the costs illustrated above, minus
Post.Discharge,Skilled.Nursing.Facility, (During.Hospitalization.Carrier,
Post.Discharge.Inpatient, and Post.Discharge.Hospice) are all statistically significant in
explaining the Readmission Rate (exhibited in this linear model, model E).
The adjusted R-squared in this model is 0.03705. That is pretty low, and can be again be
attributed to the fact that quality of care, patient attributes, and a lot of other factors would have
to be included in this model as explanatory variables, and are variables that we do not currently
have.
Regression Model Variable Importance Using Random Forest
Model F
A regression model was further run using Random Forest and all of the Numerical variables.
Please see the below for the variable importance plot:

Figure 9: Variable Importance Plot from Random Forest Learning Method.
In this model, there are some differences in regards to the variables which have been
selected compared to the decision tree above in which the dependent variable is a binary Yes or
No in terms of if the selected facility was a “Best Leader”. A standard linear model was utilized
here with the variables noted above in the call formula. Not all of the variables are significant,
however. The Adjusted R-Squared in this model is 0.5899, as opposed to Model D, which had an
Adjusted R-Squared value of 0.4786.
Model G
Log transformations had to be made of both the explanatory and response variables to
handle heteroscedasticity in model G. Additionally, a total of 444 observations had to be

excluded in our last model (model G, see below) due to influence measures on the model. All
outliers identified in the plot below have a notable difference between the numbers of billings
and the number of patients that is not reflected in the readmissions rate. This model illustrates a
strong business case for the Healthcare Provider Value Quadrant scoring. See below for the
model visualization.
Figure 10: Scatterplot of Overall Value and Risk-based Cost per Patient with Outliers.

Figure 11: Visualization of Business Case Model from Model G.
As you can see, a 1% increase in the overall value score predicts a reduced risk-based
cost per patient by 1.2%. This was created in R, where the log() function defaults to the natural
logarithm.
Model H
This model includes only the cost variables. This model is based on the four variables
identified with the Random Tree (During.Hospitalization.Carrier, Post.Discharge.Inpatient,
Post.Discharge.Carrier, and log(During.Hospitalization.Inpatient). Model H excludes 463
observations due to influence measures. This model has improved notably. R^2 is now 0.54.
However, there are some problems with the diagnostics from this model. See the appendix for
model diagnostics, followed by a discussion below.

The Normal Q-Q plot above shows curvature at the top and the bottom. The bottom may
not be acceptable, but the top is probably not. Furthermore, the Bruesch-Pagan test confirms
heteroscedasticity (BP=64.38, df=4, p-value=3.476e-13). However, using the Goldfeld-Quandt
test, the model could be accepted as the p-value is not significant (GQ=0.84684, df1=1304,
df2=1303, p-value=0.9986). Using the Harrison-McCabe test, the model could also be accepted
as the p-value is not significant (HMC=0.54107, p-value=0.997).
Model I
This is the model Team HADOC wants to use as the “final” model. The number of
observations excluded from the model due to influence measures through two iterations, such as
hat values, is 898 observations. This left 2,182 observations to build the final model with.
The Adjusted R^2 value of this model is 0.5433. This model includes the four variables
as identified by the Decision Tree Model (During.Hospitalization.Carrier,
Post.Discharge.Inpatient, Post.Discharge.Skilled.Nursing.Facility, Post.Dsicharge.Hospice), plus
the Pot.Discharge.Carrier variable, as identified by Random Forest.
There appears to be slight curvature at the top and bottom of the Normal Q-Q plot. See
below for a visualization of the model.

Figure 12: Visualization of Provider Value Model from Model I.
An R package called “visreg” was used, which provides easy visualization of models. In
visualizing the model, there are clearly no concerns of heteroscedasticity, as exemplified by the
above visualizations.
This model still gives us conflicting tests for validation. The Breusch-Pagan test=40.03
(df=5, p-value=1.473e-07), while the Goldfeld-Quandt test=0.90805 (df1=1-26, df2=1025, p-
value=0.9387), and the Harrison-McCabe test=0.52384 (p-value=0.934). The Breusch-Pagan test
indicates concerns for heteroscedasticity, while the Goldfeld-Quant and Harrison-McCabe tests
would not.

Hypothetically, to get this model down to where the Breusch-Pagan test no longer has a
significant p-value, 990 further observations would have to be excluded. This leaves only 1,073
observations to build the model (BP=10.344, df=5, p-value=0.06605). That is only 35% of the
original 3,080 observations. This is not the route team HADOC wants to take. Instead, the team
is accepting the fact that “All models are wrong but some are useful” (Box, 1979). Many benefits
are derived from this model, including using this model as prescriptive KPIs. Model I can be
paired with the Scoring and Cost per Patients model, which supports the Healthcare Provider
Value Quadrant.
Model Results
The final solution incorporates the Ranking Model as used in the Healthcare Provider
Value Quadrant (HPVQ) and the Multivariate Model as detailed above as Model I with variables
identified in the Decision Tree from Model C and the Random Forest from Model F. Model I is
used as both the key performance indicator (KPI) and prescriptive analytic metric within the
HPVQ for each of the healthcare providers included. Model G is presented as the business case
for the HPVQ.
Best Model Selection
The best model selection comes down to the application of the Gauss-Markov Theorem
(Aitken, 1935). As long as the assumptions are true, linear regression provides the best unbiased
estimates of the coefficients. In trying to build a model by starting with all the numerical
variables, a linear model was not able to be created where there were not some problems with the
diagnostics. As such, a nested model selection was not completed and we selected the linear
model with the five explanatory variables as identified through the Decision Tree in Model C
and Random Forest in Model F as detailed previously.

Ranking Healthcare Providers
The final model for ranking healthcare providers is a standardized score based on the z-
score transformation in a normal distribution for both the Overall Costs and Readmissions Rate:
𝒵𝑖 =
𝒳𝑖 − 𝜇
𝜎
Where 𝜇 is the mean of the providers and 𝜎 is the standard deviation.
The Standardized Score for both Overall Costs and Readmissions Rate is then calculated:
𝑆𝑖 = 100 + 𝒵𝑖(−15)
Where the distribution of scores has mean (𝜇) of 100 and standard deviation (𝜎) of 15 such that a
score of 115 is one standard deviation above the mean and a score of 85 is one standard deviation
below the mean. A negative transformation with 𝜎 allows the score to be reversed, so that
providers with lower readmissions rates or lower costs received higher scores.
The Overall Value score is the mean of the Costs Score and Readmissions Rate score:
𝒱𝑖 =
𝒞𝑖 + ℛ𝑖
2
Where 𝒞𝑖 is the Standardized Score (𝑆𝑖) for Overall Costs and ℛ𝑖 is the Standardized Score (𝑆𝑖)
for Readmissions Rate.
Healthcare providers are then categorized on the Healthcare Provider Value Quandrant.
The quadrant categories are:
Quadrant I: 𝒞𝑖 > 100 and ℛ𝑖 > 100  Leaders
Quadrant II: 𝒞𝑖 > 100 and ℛ𝑖 <= 100  Cost-Focused
Quadrant III: 𝒞𝑖 <= 100 and ℛ𝑖 > 100  Care-Focused
Quadrant IV: 𝒞𝑖 <= 100 and ℛ𝑖 <= 100  Sub-Performers

The Overall Value score allows for a further ranking of healthcare providers in the
following:
Top 20%  Best
60-80%  Better
40-60%  Good
20-40%  Bad
<20%  Worst
The overall healthcare provider category combines these categories and develops 16
rankings of healthcare providers:
Best: Leaders, Cost-Focused, Care-Focused
Better: Leaders, Cost-Focused, Care-Focused
Good: Leaders, Cost-Focused, Care-Focused, Sub-Performers
Bad: Cost-Focused, Care-Focused, Sub-Performers
Worst: Cost-Focused, Care Focused, Sub-Performers
As a matter of distribution, there are no Best or Better Sub-Performers and there are no
Bad or Worst Leaders.
Predicting Healthcare Provider Value
The final model for predicting healthcare provider value is a Multivariate Linear Model
with the following formula:
Ŷ = 𝛽0 + 𝛽1 𝜒1 + 𝛽2 𝜒2 + 𝛽3 𝜒3 + 𝛽4 𝜒4 + 𝛽5 𝜒5 + 𝜀
Where Ŷ is the predicted Overall Value score. The coefficients have the following values:
Estimate Std. Error p-value
(Intercept) 1.277e+02 7.505e-01 < 2e-16

𝜒1: During Hospitalization Carrier -6.250e-03 3.948e-04 < 2e-16
𝜒2: Post Discharge Inpatient -3.559e-03 2.226e-04 < 2e-16
𝜒3: Post Discharge Skilled Nursing Facility -1.291e-03 1.523e-04 < 2e-16
𝜒3: Post Discharge Hospice 1.757e-02 2.290e-03 2.51e-14
𝜒4: Post Discharge Carrier -7.991e-03 8.788e-04 < 2e-16
Each explanatory variable is highly statistically significant. 𝑅2
is 0.5443 and adjusted
𝑅2
is 0.5433. With this model, we can interpret that 54% of the Overall Value score is explained
by these five explanatory variables. The model has a residual standard error (𝜀) of 5.5 on 2176
degrees of freedom.
Predicting Reduced Risked-based Cost Per Patient
The model for predicting the risk-based cost for per patient for a provider is a Univariate
Linear Model with the following formula:
Ŷ = 𝛽0 + 𝛽1 𝜒1 + 𝜀
Where Ŷ is the predicted risk-based cost per patient. The coefficients have the following values:
Estimate Std. Error p-value
(Intercept) 15.73459 0.14768 <2e-16
𝜒1: ln(Standard.Overall.Value) -1.19954 0.03201 <2e-16
Both the explanatory and the response variables where transformed with the natural
logarithm in order to fit the linear model. The explanatory variable is highly statistically
significant. 𝑅2
is 0.3478 and adjusted 𝑅2
is 0.3476. With this model, we can interpret that 35% of
the risk-based cost per patient is explained in the Overall Value Score. Furthermore, a 1%
increase in the Overall Value Score predicts a reduced risk-based cost per patient of 1.2%. The
model has a residual standard error (𝜀) of 0.1295 on 2634 degrees of freedom.

Implementation of the Model
In regards to the implementation of the model in the healthcare provider value quadrant
(HPVQ) dashboard, the minimum overall value for a Best Leaders provider is 108.7.
Additionally, if you take the value of each of the four cost variables as identified in the decision
tree as included in Model C above (During.Hospitalizaion.Carrier, Post.Discharge.Inpatient,
Post.Discharge.Skilled.Nursing.Facility, and Post.Discharge.Hospice), and use algebra to find
the value for Post.Discharge.Carrier with the final linear model, the value for
Post.Discharge.Carrier is found to be 645. Plugging this value back into the model, you get the
following:
127.7 - .00625(1336) - .003559 (1456) - .001291 (4216) + .01757 (292) - .007991 (645) = 108.7,
as Predicted Overall Value which is the same as the minimum Overall Value for Best Leaders.
Thus, for a basic implementation of this prescriptive method, if any provider’s costs are a
higher amount for any of the four cost variables that negatively impact their score, the
prescriptive information should return that variable with the recommendation of getting the cost
below that value. For one variable that positively impacts the score (Post.Discharge.Hospice), the
prescriptive information would do the opposite: recommend spending more if their value is
below the amount.
The final prescriptive metric could be to calculate the percentage change in Overall Value
when our prospective recommendations are attained, and then to detail the percentage change in
the risk-based cost per patient by using the business case model that a 1% increase in Overall
Value reduces the risk-based cost per patient by 1.2%.

Demonstration
The solutions from this research study are included in the interactive version of the
Healthcare Provider Value Quadrant (HPVQ) which is currently able to be run in R (s future
version will be available as a Shiny App).
Figure 13: Interactive Dashboard of the Health Provider Value Quadrant.

Using the HPVQ, it is possible search according to multiple criteria to include: State,
City, Geographic Type, Ownership Type, Teaching Status, Size of Provider, and Value Quadrant
Category. For example, if select LURBAN (Large Urban geographic type), Major-Teaching, and
Large Provider Size, the results show that there are no providers in the Leaders Quadrant.
Figure 14: Displaying no Leaders of Large, Urban, Major-Teaching Providers.

In expanding the previous search to include OURBAN (Other Urban geographic type)
and Minor-Teaching, the results now include five Best Leaders, ten Better Leaders, and two
Good Leaders.
Figure 15: Displaying Few Leaders of Large, Urban, Minor-Teaching Providers.

When the mouse is hovered over a point on the graph, the information for the provider is
displayed in a pop-up.
Figure 16: Displaying Provider Details when Hovering-over a Point on the Dashboard.

When a point on the graph is clicked, prescriptive recommendations based on the
Multivariate Model to predict healthcare provider value are displayed along with the current
values for the respective variables.
Figure 17: Displaying Prescriptive Analytics when Clicking-on a Point on the Dashboard.
Use Case
The methods and models that we have developed for this research project are such that
they can be used in multiple ways for great benefit by the payers of healthcare to include
insurance, health management organizations (HMO), and employers as well as consumers and
healthcare providers themselves.

One such use case is for insurance companies to drive greater utilization of higher valued
healthcare providers. This could result in substantial return on investment (ROI) which can be
forecast along with utilization targets. The following image shows a basic template for tracking
such utilization shifts.
Figure 18: Example of Spreadsheet for Tracking Utilization Goals and Results.
Challenges
Several challenges and limitations presented themselves with this project, as with any
project of course. With preventative planning, Team HADOC dealt with these limitations and
challenges and drove forward to come up with the best valued results for the analysis that the
team could.
One challenge is defining the term health care “quality”, which can have many different
interpretations. Defining the term quality is rather difficult, especially when talking about
medical services. In our analysis, we limited the term “quality” to being defined as the 30-day
readmission rate, and the team did not consider other elements of quality, such as patient

outcomes and/or quality of life considerations, both of which would go way beyond the scope of
the data that was available to the team.
The team was limited to the Center for Medicare and Medicaid Services data. We did not
have access to other medical data for instance, data for private insurance provided or data on
consumer paid for services. Another limitation includes the scope of the data. The readmissions
data used covers a three year span, whereas the demographic data covers only a one year span.
Another limitation is the need to ensure that consumers have adequate access to the data
and information in order to make informed healthcare choices. Ensuring that consumers have
adequate access to the data available and the information available to make informed healthcare
decisions is very difficult. It is generally tough to make grandiose decisions in healthcare based
on the little information you are provided. Most people generally only work with a primary care
physician, and for most of us, we trust that primary care physician due to the relationship we
have forged over the years. Someone who has dealt with one of the disease groups in this study
has probably worked with their primary care physician for multiple years, not just for the period
of time associated with having this disease. Thus, making sure consumers have adequate
information in order to make informed healthcare decisions is difficult and can be limiting.
For this project, there was time constrains of completing the full project within a ten
week span, while at the same time having a team that has conflicting priorities, including
working in different time zones both within the United States and outside the United States, with
one group member being stationed in Afghanistan. This presents additional challenges and
limitations in the analysis but the team does think that we have created very valuable and
interesting results that can be built upon for further analysis going forward.

Opportunities
A Value-Based Payment Modifier Program exists with the Centers for Medicare and
Medicaid Services. The 30-day All-Cause Hospital Readmission measure is a risk-standardized
readmissions rate for beneficiaries age 65 or older who were hospitalized at a short-stay acute
care hospital and experienced an unplanned readmission for any cause to an acute care hospital
within 30 days of discharge, according to the CMS website. A hospital-level quality measure
already exists, developed at the Yale School of Medicine for CMS. Poor quality of care,
inadequate coordination of care, and lack of effective discharge planning and transitional care
can all lead to higher readmissions, keeping in mind some readmissions are unavoidable. CMS
intends to reduce avoidable readmissions with its Value Modifier. By incorporating the model
produced by Team HADOC, CMS could have a way to quantify and effectively choose which
hospitals provide the best value to consumers, while at the same time comparing avoidable
readmissions criteria by using the Taxpayer Identification Number, or TIN, which applies to solo
practitioners and groups of practitioners.
Another opportunity identified by Team HADOC would be to switch the focus from fee-
for-service to value-based reimbursement for healthcare providers. According to Fierce Health
Finance, the health industry’s move away from the traditional fee-for-service model is going
slower than anticipated, and is coupled with numerous challenges. The government has launched
a program to speed up the transition to value-based care, which includes Medicare tying 30
percent of all fee-for-service payments to quality incentives through alternative payment models
by 2016. By using our model to find out which facilities give the best value for patients, the
government could potentially figure out which markets best suit the transition to value-based
care models, and thus figure out which qualities of these markets drive these results.

Recommendations
Below are some recommendations team HADOC has made going forward, which include
short term and long term suggestions. The Team recommends that a greater emphasis be placed
on value-based reimbursement for healthcare rather than just direct fee-for-services. Looking at
the outcomes of healthcare would be, in the long term, more beneficial to the quality of the care
as well as the cost of the healthcare provided.
Looking at value beyond just the typical monetary measures that are used by healthcare
providers, for example the different healthcare providers that may have different categorizations,
such as being a for-profit versus not-for-profit type organization as well as looking at healthcare
costs, a facility has to balance their business priorities as well, such as meeting their margin.
Looking at value from the prospective of the provider would also be a very interesting
consideration in addition to how team HADOC looked at value from the prospective of the
person or persons who are paying for the healthcare.
Lastly, team HADOC would strongly recommend that there be case studies conducted on
individual providers that are among the Best Leaders category, especially those that would be
considered outliers. One of our earlier decision trees identified that small, rural providers are
among the best leaders, but there are best leaders that are larger healthcare providers, there are
leaders that are within urban environments. These leaders would be very interesting to study in
order to identify additional variables or additional factors for the reasons why they are one of the
Best Leaders that are not identified in the available data set that we have available. Thus, these
case studies could be very useful.

Project Review
Over the past ten weeks Team HADOC and The Advisory Board have worked in
collaboration on the readmission reduction model. The initial project charter called for the
project to assess the relationship between hospital location, patient demographics, and
reimbursement to overall patient outcomes. These findings were to be stratified by disease group
to help payers drive patients to hospitals with favorable outcomes. Additionally, the project
sought to assess the relationship between hospital location, patient demographics, and
reimbursement to overall patient outcomes.
Over the ten weeks as the team explored the data available, a more global question began
to surface around healthcare value. It appeared that there was an opportunity to more fully
understand the value equation between healthcare costs and healthcare quality. The team slightly
changed its approach and refined the end goal as it explored the research questions and initial
hypothesis.
Initial Research questions for the study were as follows:
1. Has current research identified all the explanatory factors for readmission?
o Ultimately the team steered away from the specific explanatory factors for
readmissions and focused more on the dynamics between cost, readmissions, and
value.
2. Is the best medicine provided with methods to address readmission penalties?
o Again, the team shifted here from the concept of medical care to that of value.
3. Is healthcare more or less costly at hospitals adversely impacted by patient characteristics
(specifically poverty and social support) known to have a higher probability of
readmission?

o The team took a very deep dive into this concept. In fact it found that there were
relationships between cost and care. However, it was imperative to look at total
cost of care per patient rather than simple episodic care. There was significant
variation across facilities that treated patients in the same geographic area.
Exploiting these differences is a foundation of the study as the outcome seeks to
drive patients to higher quality facilities and for payers to identify, and mitigate
under-performing providers.
4. Does the payer of healthcare receive better care if they pay more for it?
o Not always. There were a significant number of organizations that had high cost
care, yet quality outcomes were not better than other providers. This value
proposition was a backbone of the project.
5. Does the payer of healthcare receive better care when hospitals are penalized for higher
readmissions?
o Performance on readmission penalties was considered, but the team shifted
perspective here slightly. As a follow-up to this study, the team has determined
that future analysis specific to the relationships and drivers of actual, and risk
adjusted, readmissions would be a prudent next step in evaluating and improving
healthcare efficiency.
The other look back is on the impact the study had in addressing the initial hypotheses.
There were two primary hypotheses that initially drive our research. First, that paying more for
healthcare will result in better care provided. The second primary hypothesis is that the 3%
penalty for higher readmissions by Medicare will result in better care provided at a lower cost to
the payer of healthcare.

Ultimately at the conclusion of the analysis it was determined that paying more for
healthcare did not guarantee better outcomes. Additionally, it was determined that in fact higher
cost teaching hospitals often had some of the worst patient outcomes. Each hospital was unique,
but there was tremendous variability between providers in similar markets. It highlights what
was identified by the research from the Commonwealth fund that shows that cost of care in the
United States is higher with no significant difference in quality. There is simply too much
variation in care and to solve the healthcare concerns this has to be addressed, or at least
incentivize consumers to seek care at top performers.
In addition to the review of the analysis compared to the initial project charter, the tables
below compare the milestone and cost schedules compared to actual performance.
Summary Milestone Schedule- Revised with Actual Dates
Project Milestone Target Date Actual Date
 Team Kickoff 09/28/2015 9/28/2015
 Project and Analysis Plan 10/04/2015 10/3/2015
 Solution Development and Implementation Report – Part 1 10/18/2015 10/18/2015
 Solution Development and Implementation Report – Part 2 11/01/2015 11/01/2015
 Presentation of work and Dashboard of Implemented Solution 11/29/2015 11/29/2015
 Final Project Report 12/06/2015 12/06/2015
 Project Complete 12/06/2015 12/06/205
Table 2: Project Review Summary of Milestones.
Summary Budget – Revised Upon Project Completion
Project Component Projected Cost Actual Cost
 Personnel Resources $75,000 $71,000
 Telecommunications and Travel $3,000 $972
 Printing $1,000 $322
 Total Budget Including Dollars at Risk $79,000 $72,294
Table 3: Project Review Summary of Budget.

Conclusion
In conclusion, Team HADOC has successfully developed a way to quantify value
captured in health care facilities using multiple models and an interactive dashboard. A Ranking
Model for Provider Value which is used to build the quadrant, a Decision Tree to include all
variables in the data set, a Decision Tree that excludes attributes which providers cannot change,
and Linear Models to validate and further expound upon the decision trees and ranking model
were all utilized by Team HADOC and discussed above to illustrate why healthcare facilities
should highly considered switching from fee-for-service to value-based care. The final models
selected for the best solution include the ranking model to sort healthcare providers on a
quadrant and further categorize from Best to Worst value, a Multivariate Linear Model to predict
the value of healthcare providers and enable prescriptive analytic recommendations, and a
Univariate Linear Model that predicts a reduced risk-based cost per patient of 1.2% for every 1%
increase in the value score used to rank healthcare providers. Finally, this solutions provides a
clear business case and simple implementation for the use of the models by insurance companies,
healthcare management organizations, and consumers to foster value from healthcare providers.

References
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Appendix A
Table 1.0 Provider Location, Payment and Demographic Information
Provider Number 6 digit Medicare provider number; the first 2 digits are the state code.
Name Name of Medicare provider from OSCAR.("blank" = unknown)
Geographic Labor Market Area The Geographic CBSA location based on OMB's Core Based Statistical Area (CBSA) designations. The CBSA
assignment is based on where the provider is physically located based on SSA state and county code
information. Rural areas are designated by 2-digit SSA state codes.
Pre Reclass Labor Market Area Pre-reclassification CBSA
Payment Labor Market Area (for
purposes of Capital and DSH)
Payment CBSA (urban vs rural) for purposes of determining capital & DSH payments
SSA COUNTY CODE SSA state county code. First two digits represent the state code and the last 4 digits represent the county code. SSA
system is used to Identify the county in which provider is geographically located and this field be used in conjunction
with the msa/cbsa crosswalk file.
REGION 1=NEW ENGLAND; 2=MIDDLE ATLANTIC; 3=SOUTH ATLANTIC; 4=EAST NORTH CENTRAL; 5=EAST
SOUTH CENTRAL; 6=WEST NORTH CENTRAL; 7=WEST SOUTH CENTRAL; 8-=MOUNTAIN; 9=PACIFIC;
40=PUERTO RICO
URGEO Large urban, Other Urban or Rural designation of the providers geographic CBSA
URSPA Urban or Rural designation based on payment CBSA
RECLASS Reclass Status FY 2016: N -provider did not reclassify; W -provider reclassified for wage index ; L provider
reclassified under 1886(d)(8)(B) of the SSA; S-provider re-designated as rural under Sec. 401 of BIPA.
POST RECLASS Labor Market Area post Reclassification CBSA for FY 2016
FY 2016 Wage Index FY 2016 wage index after applying the MGCRB reclassifications, rural floor, adjustments for the Frontier wage index
provision and the P.L. 108-173 Sec 505 adjustments where applicable for FY 2016. Wage Index reflects the
application of national rural floor budget neutrality required under the Affordable Care Act .
FY 2016 Puerto Rico Specific wage
index
FY 2016 Puerto Rico Specific wage index after applying the MGCRB reclassifications, rural floor and the P.L. 108-
173 Sec 505 adjustments where applicable for FY 2016. Wage Index reflects the application of national rural floor

budget neutrality required under the Affordable Care Act.
LUGAR Provider is located in a Lugar County as defined in 1886(d)(8)(B) of the Act
Section 401 hospital A 'YES' denotes urban providers re-designated as rural under CFR 412.103 - Sec 401 of BIPA
Section 505 eligible A 'YES' denotes providers eligible to receive a wage index adjustment under Sec. 505 of P.L. 108-173 for FY 2016
Section 505 wage adjustment Wage adjustment for providers who are eligible to receive a wage index adjustment under Sec. 505 of P.L. 108-173
for FY 2016.
Cost of Living Adjustment Cost of Living Adjustment factor obtained from the U.S. Office of Personnel Management for IPPS providers located
in Alaska or Hawaii for IPPS operating payments
Resident to Bed Ratio Resident to Bed Ratio. Used to determine IME factor for operating PPS payments
RDAY Resident to Average Daily Census (ADC) ratio. Used to calculate the IME adjustment for Capital PPS
BEDS The number of total beds obtained from cost report data.**
Average Daily Census Calculated as the ratio of Total Acute Inpatient Days to Total Days in the Cost Reporting Period obtained from cost
report data.**
TCHOP IME adjustment factor for Operating PPS
TCHCP IME adjustment factor for Capital PPS
DSHPCT Disproportionate Share Hospital Patient Percentage as determined from most recent cost report data & SSA data
DSHOPG Operating Disproportionate Share Hospital (DSH) adjustment. Reflects a 75% reduction to the DSH adjustment
required under Section 3333 of the Affordable Care Act
UCP_ADJ Uncompensated Care Payment Factor is the proportion of the additional payment amount for uncompensated care
costs that a DSH hospital will receive under the implementation of Section 3133 of the Affordable Care Act. The
Uncompensated Care Payment Factor is the hospital's uncompensated care costs relative to all DSH
hospital's uncompensated care costs. DSH hospitals are identified as those hospitals that are projected to receive DSH
for FY 2016. Note, these amounts do not reflect any changes that may result from the 30-day period after the
publication of the final rule for hospitals to review and submit comments on the accuracy of the list of mergers we
identified in the final rule.

UCP Per Claim Amount FY 2016 Uncompensated Care Per Claim Amount based on a hospital's assigned Uncompensated Care Payment
amount divided by the 3 year claims average based on MedPARs from FY11-FY14. Per Claim Amount is used in
determination of outliers and used in to determine if the SCH is paid on a hospital specific rate or federal rate on a per
claim basis. Note, these amounts do not reflect any changes that may result from the 30-day period after the
publication of the final rule for hospitals to review and submit comments on the accuracy of the list of mergers we
identified in the final rule.
DSHCPG Capital Disproportionate Share (DSH) adjustment
Operating CCR Ratio of Medicare operating costs to Medicare covered charges from the December 2014 update of the Provider
Specific File (PSF). CCRs do not have the inflation factor applied.
Capital CCR Ratio of Medicare capital costs to Medicare covered charges from the December 2014 update of the Provider Specific
File (PSF). CCRs do not have the inflation factor applied.
Provider Type for FY 2016 Type of provider - key: 0=IPPS; 7=RURAL REFERRAL CENTER (RRC); 16=Sole Community Hospital (SCH);
17=SCH/RRC; 21=ESSENTIAL ACCESS CMTY HSP (EACH); 22=EACH/RRC
Provider Type for first half of FY 2015 Type of provider - key: 0=IPPS; 7=RURAL REFERRAL CENTER (RRC);14=Medicare Dependent Hospital
(MDH); 15=MDH/RRC; 16=Sole Community Hospital (SCH); 17=SCH/RRC; 21=ESSENTIAL ACCESS CMTY
HSP (EACH); 22=EACH/RRC; Reflects extension of Medicare Dependent Hospital payment status through mid
year of FY 2015 where providers have the provider type of 14 or 15
Provider Type for second half of FY
2015
Reflects expiration of Medicare Dependent Hospital payments status after March 31, 2015
FY16 HSP Rate Hospital Specific Payment (HSP) Rate updated to FY2016 for SCH providers.
BILLS Total number of Medicare cases for the provider from the FY2014 MEDPAR, December 2014 Update
TACMIV32 Transfer adjusted Case Mix Index under Grouper V32
CASETA32 Transfer Adjusted Cases under Grouper V32 and FY 2016 Post Acute Transfer Policy
CMIV32 Case Mix Index under Grouper V32 for SCH/ former MDH providers paid under their Hospital Specific Payment rate
IME_TACMIV32 Transfer adjusted Case Mix under Grouper V32 for Medicare Advantage cases submitted by teaching hospitals that
received a Fee-for-Service IME payment. These CMIs are included to calculate the IME payments for budget
neutrality.

IME_CASETA32 Transfer adjusted cases under Grouper V30 for Medicare Advantage cases submitted by teaching hospitals that
receive a fee-for-service IME payment. The IME payment associated with these cases are included in the budget
neutrality calculations and in payment modelling.
TACMIV33 Transfer adjusted Case Mix Index under Grouper V33
CASETA33 Transfer Adjusted Cases under Grouper V33 and FY2016 Post Acute Transfer Policy
CMIV33 Case Mix Index under Grouper V33 for SCH providers paid under their Hospital Specific Payment rate
IME_TACMIV33 Transfer adjusted Case Mix under Grouper V33 for Medicare Advantage cases submitted by teaching hospitals that
received a Fee-for-Service IME payment. These CMIs are included to calculate the IME payments for budget
neutrality.
IME_CASETA33 Transfer adjusted cases under Grouper V33 for Medicare Advantage cases submitted by teaching hospitals that
receive a fee-for-service IME payment. The IME payment associated with these cases are included in the budget
neutrality calculations and in payment modelling.
FY 2016 GAF Post Reclass Geographic adjustment factor (GAF) for Capital FY 2016
FY 2016 Puerto Rico Specific GAF Post Reclass GAF for Capital for Puerto Rico Providers FY 2016
Cost of Living Adjustment-Capital Capital COLA factor for hospitals located in Alaska and Hawaii, which is based on the applicable operating IPPS
COLA factor .
OUT16F Estimated operating outlier payments as a percentage of the provider's Federal operating PPS payments
COUT16F Estimated capital outlier payments as a percentage of the provider's Federal capital PPS payments
MCR_PCT Medicare days as a percent of total inpatient days (not available for all providers that receive HSP rate)
Proxy Value Based Purchasing
Adjustment Factor
Proxy payment adjustment for value based purchasing program (Section 3001 of Affordable Care Act) based on
performance scores from an older performance period.
Proxy Readmission Adjustment Factor Proxy payment adjustment for Hospital Readmissions Reduction Program (Section 3025 of Affordable Care Act).
Maryland and Puerto Rico hospitals are exempt from the payment adjustment.
Quality Reduction Value of '1' indicates a hospital that was found not to have submitted quality data in the form and manner specified by
CMS, and therefore received a reduction to the percentage increase in the market basket index for FY 2015 under

§412.64(d)(2).
EHR Reduction Value of '1' indicates a hospital that was found not to be a meaningful electronic health record (EHR) user for the
applicable EHR reporting period and did not receive an exception, and therefore received a reduction to the percentage
increase in the market basket index for FY 2015 under §§ 412.64(d)(3)-(4).

Table 2.0 Hospital Readmission Performance
Total Patients Total Medicare patients eligible for a readmission
Readmission Rate Overall hospital all cause readmission rate
READM-30-AMI-HRRP Discharges The total number of patients in the AMI (heart attack) patient population, or denominator, for inclusion
in the readmission rate.
READM-30-COPD-HRRP Discharges Total patients in the chronic obstructive pulmonary disease patient population, or denominator, for
inclusion in the readmission rate.
READM-30-HF-HRRP Discharges Total heart failure patient population, or denominator, for inclusion in the readmission rate.
READM-30-HIP-KNEE-HRRP Discharges Total patients in the total joint replacement patient population, or denominator, for inclusion in the
readmission rate.
READM-30-PN-HRRP Discharges Total patients in the pneumonia patient population, or denominator, for inclusion in the readmission rate.
READM-30-AMI-HRRP Ratio Total heart attack predicted readmissions divided by expected readmissions
READM-30-COPD-HRRP Ratio Total COPD predicted readmissions divided by expected readmissions
READM-30-HF-HRRP Ratio Heart failure predicted readmissions divided by expected readmissions
READM-30-HIP-KNEE-HRRP Ratio Total joint replacement predicted readmissions divided by expected readmissions
READM-30-PN-HRRP Ratio Total pneumonia predicted readmissions divided by expected readmissions
READM-30-AMI-HRRP Predicted Readmissions The predicted/actual readmission rate for heart attack patients.
READM-30-COPD-HRRP Predicted Readmissions The predicted/actual readmission rate for COPD patients.
READM-30-HF-HRRP Predicted Readmissions The predicted/actual readmission rate for heart failure patients.
READM-30-HIP-KNEE-HRRP Predicted
Readmissions
The predicted/actual readmission rate for total joint patients.
READM-30-PN-HRRP Predicted Readmissions The predicted/actual readmission rate for pneumonia patients.

READM-30-AMI-HRRP Expected Readmissions The expected readmission rate for heart attack patients.
READM-30-COPD-HRRP Expected Readmissions The expected readmission rate for COPD patients.
READM-30-HF-HRRP Expected Readmissions The expected readmission rate for heart failure patients.
READM-30-HIP-KNEE-HRRP Expected
Readmissions
The expected readmission rate for total joint patients.
READM-30-PN-HRRP Expected Readmissions The expected readmission rate for pneumonia patients.

Table 3.0 Medicare Spending per Beneficiary
Post Discharge Carrier The cost of durable medical equipment during a hospitalization
Post Discharge Durable Medical Equipment The cost of durable medical equipment within 30 days post discharge
Post Discharge Home Health Agency The cost of home health expenses within 30 days post discharge
Post Discharge Hospice The cost of hospice care within 30 days post discharge
Post Discharge Inpatient The cost of inpatient care within 30 days post discharge
Post Discharge Outpatient The cost of outpatient care within 30 days post discharge
Post Discharge Skilled Nursing Facility The cost of skilled nursing facility care within 30 days post discharge
Total Post Discharge Total amount spent on healthcare in the 30 days post discharge
Pre Admission Carrier The cost of carrier related costs medical equipment within 1-3 days prior to admission
Pre Admission Durable Medical Equipment The cost of durable medical equipment within 1-3 days prior to admission
Pre Admission Home Health Agency The cost of home health expenses within 1-3 days prior to admission
Pre Admission Hospice The cost of hospice care within 1-3 days prior to admission
Pre Admission Inpatient The cost of inpatient care within 1-3 days prior to admission
Pre Admission Outpatient The cost of outpatient care within 1-3 days prior to admission
Pre Admission Skilled Nursing Facility The cost of skilled nursing facility care within 1-3 days prior to admission
Pre Admission Total amount spent on healthcare in the1-3 days prior to an admission
Overall Spending Total amount spent per episode of care. This number is risk adjusted based on patient population.
During Hospitalization Carrier The cost of carrier related costs medical equipment during a hospitalization
During Hospitalization Durable Medical Equipment The cost of durable medical equipment during a hospitalization

During Hospitalization Inpatient The inpatient cost of the inpatient admission
During Hospitalization Total Total amount spent on healthcare during a hospital admission
Total Spend Ratio The ratio of spending to the national median. A number above one means more spend and less than one
represents less spending per patient

Table 4.0 Hospital Charges for Services Rendered
Avg.Standard.Covered.Charges Average standardized score of the provider's average charge for services covered by Medicare for all discharges in the
DRG
Avg.Standard.Total.Payments Average standardized score of the average total payments to all providers for the MS-DRG including the MSDRG amount,
teaching, disproportionate share, capital, and outlier payments for all cases
Avg.Standard.Medicare.Payments Average standardized score of the average amount that Medicare pays to the provider for Medicare's share of the MS-DRG
StDev.Standard.Covered.Charges Standard deviation of the averaged standardized score for the Average Standard Covered Charges
StDev.Standard.Total.Payments Standard deviation of the averaged standardized score for the Average Standard Total Payments
StDev.Standard.Medicare.Payments Standard deviation of the averaged standardized score for the Average Standard Medicare Payments
Quantity.Of.Cost.Entries Number of entries in the IPPS Cost Data for FY2011, FY2012, FY2013 used for the average standardized scores

Appendix B
Table 5.0 Variables Removed From Analysis
Variable Name Reason for Proposed Removal from Variable Set
Name This is a second facility identifier. Name will be helpful upon output, but ultimately will not be needed as Medicare
provider ID will be the unique identifier.
City City identifiers will not be needed because we will use regions. Medicare provider IDs will remain the unique identifier.
FY 2016 Puerto Rico
Specific wage index
Excluding all variables related to Puerto Rico as their patient outcomes data is not available.
HHFLAG Only 8 entries in the data set had information for this variable. Recommend removing this variable.
LUGAR Only 55 entries in the data set had information for this variable. Recommend removing this variable.
Section 401 hospital Only 58 entries in the data set had information for this variable. Recommend removing this variable.
Section 505 wage adjustment Only 338 entries in the data set had information for this variable. Recommend removing this variable.
Section 505 eligible Only 338 entries in the data set had information for this variable. Recommend removing this variable.
Cost of Living Adjustment Only 21 entries have unique performance. Recommend removal.
UCP_ADJ 1,069 are missing records. This is more than 33% of the total entries and therefore recommends removing.
Provider Type for first half of FY
2015
Provider type for 2016 is an additional variable. Therefore recommend removing provider type for first half and second
half of FY 2015.
Provider Type for second half of
FY 2015
Provider type for 2016 is an additional variable. Therefore recommend removing provider type for first half and second
half of FY 2015.
FY16 HSP Rate Only 455 entries have a value here meaning more than 2/3 of the data set does not. Recommend removing. Option B would
be to create a missing variable flag.
TACMIV32 Correlation between grouper 32 and 33 is 0.99 and therefore to avoid multicollinearity recommend removing and using
grouper 33.

CASETA32 Correlation between grouper 32 and 33 is 0.99 and therefore to avoid multicollinearity recommend removing and using
grouper 33.
CMIV32 Correlation between grouper 32 and 33 is 0.99 and therefore to avoid multicollinearity recommend removing and using
grouper 33.
IME_TACMIV32 Correlation between grouper 32 and 33 is 0.99 and therefore to avoid multicollinearity recommend removing and using
grouper 33.
IME_CASETA32 Correlation between grouper 32 and 33 is 0.99 and therefore to avoid multicollinearity recommend removing and using
grouper 33.
IME_TACMIV33 Only 999 entries have a value here meaning more than 2/3 of the data set does not. Recommend removing. Option B would
IME_CASETA33 Only 999 entries have a value here meaning more than 2/3 of the data set does not. Recommend removing. Option B would
FY 2016 Puerto Rico Specific GAF
- Revised
Excluding all variables related to Puerto Rico as their patient outcomes data is not available.
Cost of Living Adjustment-Capital Only 21 entries have unique performance. Recommend removal.
Quality Reduction Only 30 hospitals impacted and therefore recommend removal of this variable.
EHR Reduction Only 145 hospitals impacted and therefore recommend removal of this variable.
Total Patients Correlation between total patients and bills is 0.98 and therefore to avoid multicollinearity recommend removing this
variable and using total bills.
Readmission Rate Recommend removing because there is no way to risk adjust this measure.
Pre Admission Carrier Only 14 of the 3,444 entries spent 2% or more on this area. As such only a small percentage of healthcare dollars are spent
here and removal is recommended.
Pre Admission Durable Medical
Equipment
No entity spent more than 0.75% of total healthcare spending on this area. As such removal is recommended.
Pre Admission Home Health Only 1 facility out of 3,444 entries spent 1% of healthcare dollars on this area. As such removal is recommended

Agency
Pre Admission Hospice No entity spent more than 0.60% of total healthcare spending on this area. As such removal is recommended.
Pre Admission Inpatient Only 2 facilities out of 3,444 entries spent 1% of healthcare dollars on this area. As such removal is recommended
Pre Admission Outpatient Only 8 of the 3,444 entries spent 2% or more on this area. As such only a small percentage of healthcare dollars are spent
here and removal is recommended.
Pre Admission Skilled Nursing
Facility
No entity spent more than 0.30% of total healthcare spending on this area. As such removal is recommended.
Pre Admission Recommend removing because of the low percentage of total healthcare spend related to pre admissions.

Table 6.0 Variable Breakdown
Variable Variable Type Min. 1st Qu. Median Mean 3rd Qu. Max Missing
Variable Name [5]: Geographic.Labor.Market.Area Type: integer 1 67 24940 22800 36420 49740
Variable Name [6]: Pre.Reclass.Labor.Market.Area. Type: integer 1 99 24860 22720 36420 49740
Variable Name [7]: POST.RECLASS.Labor.Market.Area Type: integer 1 13140 26900 24450 36420 49740
Variable Name [8]:
Payment.Labor.Market.Area..for.purposes.of.Capital.and.DSH. Type: integer 1 51 23840 22270 36260 49740
Variable Name [9]: SSA.County.Code Type: integer 1000 11880 26240 26550 39620 53200 1
Variable Name [10]: Region Type: integer 1.000 3.000 5.000 5.621 7.000 40.000
Variable Name [11]: URGEO Type: character 3444 character character
Variable Name [12]: URSPA Type: character 3444 character character
Variable Name [13]: RECLASS Type: character 3444 character character
Variable Name [14]: FY.2016.Wage.Index Type: numeric 0.3894 0.8401 0.9315 0.9775 1.0340 1.9340
Variable Name [15]:
FY.2016.Puerto.Rico.Specific..wage.index Type: numeric 0 0 0 0.01476 0 1.082
Variable Name [16]: HHFLAG Type: character 3444 character character
Variable Name [17]: LUGAR Type: character 3444 character character
Variable Name [18]: Section.401.hospital Type: character 3444 character character
Variable Name [19]: Section.505.wage.adjustment Type: numeric 0.0001 0.0056 0.0126 0.0207 0.0259 0.1092 3106
Variable Name [20]: Section.505.eligible Type: character 3444 character character
Variable Name [21]: Cost.of.Living.Adjustment Type: numeric 1.000 1.000 1.000 1.001 1.000 1.250
Variable Name [22]: Resident.to.Bed.Ratio Type: numeric 0 0 0 0.06242 0.02508 1.452
Variable Name [23]: RDAY Type: numeric 0 0.0000 0.0000 0.1001 0.0452 1.5000
Variable Name [24]: BEDS Type: integer 1.0 61.0 133.0 188.5 251.0 1928.0
Variable Name [25]: Average.Daily.Census Type: integer 0.0 20.0 63.0 109.5 148.0 1695.0 25
Variable Name [26]: TCHOP Type: numeric 0 0 0 0.03048 0.01361 0.5912
Variable Name [27]: TCHCP Type: numeric 0 0 0 0.03131 0.01284 0.527
Variable Name [28]: DSHPCT Type: numeric 0 0.1722 0.2544 0.2818 0.3536 1.4270
Variable Name [29]: DSHOPG Type: numeric 0 0.00986 0.02552 0.02961 0.03535 0.2241
Variable Name [30]: UCP_ADJ Type: numeric 0 0.0001 0.0002 0.0004 0.0005 0.0074 1069
Variable Name [31]: UCP.Per.Claim.Amount Type: numeric 0.0 0.0 392.9 1022.0 723.7 663100
Variable Name [32]: DSHCPG Type: numeric 0 0 0.02177 0.03587 0.0601 0.2795
Variable Name [33]: Operating.CCR Type: numeric 0.024 0.212 0.296 0.326 0.394 1.175
Variable Name [34]: Capital.CCR Type: numeric 0.001 0.016 0.024 0.03043 0.036 0.173
Variable Name [35]: Provider.Type.for.first.half.of.FY.2015 Type: integer 0.000 0.000 0.000 3.361 7.000 22.000

Variable Name [36]:
Provider.Type.for.second.half.of.FY.2015 Type: integer 0.000 0.000 0.000 2.668 0.000 22.000
Variable Name [37]: Provider.Type.for.FY.2016 Type: integer 0.000 0.000 0.000 2.657 0.000 22.000
Variable Name [38]: FY16.HSP.Rate Type: numeric 0 5406 6181 6401 7147 17890 2989
Variable Name [39]: BILLS Type: integer 1.0 654.8 1782.0 2725.0 3872.0 37270
Variable Name [40]: TACMIV32 Type: numeric 0.5643 1.3020 1.5100 1.5330 1.7110 4.3850
Variable Name [41]: CASETA32 Type: numeric 1 639 1750 2679 3806 36910
Variable Name [42]: CMIV32 Type: numeric 0.5643 1.3120 1.5250 1.5440 1.7230 4.3810
Variable Name [43]: IME_TACMIV32 Type: numeric 0.6752 1.4930 1.6750 1.6800 1.8350 3.4320 2413
Variable Name [44]: IME_CASETA32 Type: numeric 1.0 481.2 992.4 1422.0 1901.0 12810 2413
Variable Name [45]: TACMIV33 Type: numeric 0.5836 1.3020 1.5100 1.5330 1.7090 4.4030
Variable Name [46]: CASETA33 Type: numeric 1.0 640.1 1752.0 2682.0 3809.0 36940
Variable Name [47]: CMIV33 Type: numeric 0.5836 1.3120 1.5230 1.5440 1.7210 4.3990
Variable Name [48]: IME_TACMIV33 Type: numeric 0.7097 1.4920 1.6790 1.6820 1.8410 3.4250 2413
Variable Name [49]: IME_CASETA33 Type: numeric 1.0 481.5 993.3 1423.0 1902.0 12810 2413
Variable Name [50]: FY.2016.GAF...Revised Type: numeric 0.5242 0.8875 0.9526 0.9795 1.0230 1.5710
Variable Name [51]:
FY.2016.Puerto.Rico.Specific.GAF...Revised Type: numeric 0 0 0 0.01477 0 1.055
Variable Name [52]: Cost.of.Living.Adjustment.Capital Type: numeric 1.000 1.000 1.000 1.000 1.000 1.079
Variable Name [53]: OUT16F Type: numeric 0 0.00746 0.02103 0.0474 0.04643 9.273 78
Variable Name [54]: COUT16F Type: numeric 0 0.007 0.02433 0.07612 0.06158 7.065 78
Variable Name [55]: MCR_PCT Type: numeric 0.00065 0.2645 0.3537 0.3664 0.4391 7.54 154
Variable Name [56]:
Proxy.Value.Based.Purchasing.Adjustment.Factor Type: numeric 0.9855 0.9977 1.0000 1.0010 1.0040 1.0240 97
Variable Name [57]: Proxy.Readmission.Adjustment.Factor Type: numeric 0.97 0.9927 0.9973 0.9951 0.9998 1.0000 97
Variable Name [58]: Quality.Reduction Type: integer 0 0 0 0.01597 0 1
Variable Name [59]: EHR.Reduction Type: integer 0 0 0 0.05168 0 1
Variable Name [60]: Total.Patients Type: numeric 26.0 509.2 1324.0 2001.0 2798.0 26640 148
Variable Name [61]: Readmission.Rate Type: numeric 11.30 14.70 15.20 15.25 15.80 19.80 148
Variable Name [62]: READM.30.AMI.HRRP.Discharges Type: numeric 0.0 0.0 57.0 142.1 216.5 1667.0 105
Variable Name [63]: READM.30.COPD.HRRP.Discharges Type: numeric 0.0 90.0 211.0 268.7 387.0 2740.0 105
Variable Name [64]: READM.30.HF.HRRP.Discharges Type: numeric 0.0 97.0 238.0 339.6 482.0 3570.0 105
Variable Name [65]:
READM.30.HIP.KNEE.HRRP.Discharges Type: numeric 0.0 0.0 0.0 222.4 351.0 6793.0 105
Variable Name [66]: READM.30.PN.HRRP.Discharges Type: numeric 0.0 111.0 228.0 283.2 395.0 2430.0 105
Variable Name [67]: READM.30.AMI.HRRP.Ratio Type: numeric 0 0.0000 0.9555 0.6624 1.0190 1.2540 105

Variable Name [68]: READM.30.COPD.HRRP.Ratio Type: numeric 0 0.9381 0.9874 0.8861 1.0330 1.3460 105
Variable Name [69]: READM.30.HF.HRRP.Ratio Type: numeric 0 0.9256 0.9871 0.8937 1.0460 1.3890 105
Variable Name [70]: READM.30.HIP.KNEE.HRRP.Ratio Type: numeric 0 0.6782 0.9447 0.7597 1.0560 1.9100 105
Variable Name [71]: READM.30.PN.HRRP.Ratio Type: numeric 0 0.9324 0.9850 0.9051 1.0410 1.2790 105
Variable Name [72]:
READM.30.AMI.HRRP.Predicted.Readmissions Type: numeric 0.00 0.00 16.00 12.03 18.90 30.10 105
Variable Name [73]:
READM.30.COPD.HRRP.Predicted.Readmissions Type: numeric 0.00 18.10 19.70 17.88 21.30 31.00 105
Variable Name [74]:
READM.30.HF.HRRP.Predicted.Readmissions Type: numeric 0.00 19.80 21.80 19.89 23.70 32.80 105
Variable Name [75]:
READM.30.HIP.KNEE.HRRP.Predicted.Readmissions Type: numeric 0.000 3.200 4.800 4.036 5.700 10.800 105
Variable Name [76]:
READM.30.PN.HRRP.Predicted.Readmissions Type: numeric 0.00 15.10 16.70 15.46 18.30 27.90 105
Variable Name [77]:
READM.30.AMI.HRRP.Expected.Readmissions Type: numeric 0.00 0.00 16.30 11.99 18.60 28.00 105
Variable Name [78]:
READM.30.COPD.HRRP.Expected.Readmissions Type: numeric 0.00 18.70 20.00 17.84 21.00 29.40 105
Variable Name [79]:
READM.30.HF.HRRP.Expected.Readmissions Type: numeric 0.00 20.90 22.10 19.84 23.00 28.00 105
Variable Name [80]:
READM.30.HIP.KNEE.HRRP.Expected.Readmissions Type: numeric 0.000 3.950 5.000 3.998 5.500 9.200 105
Variable Name [81]:
READM.30.PN.HRRP.Expected.Readmissions Type: numeric 0.00 15.80 16.80 15.42 17.85 25.00 105
Variable Name [82]: Post.Discharge.Carrier Type: numeric 167.0 692.2 869.0 896.0 1073.0 2576.0 258
Variable Name [83]:
Post.Discharge.Durable.Medical.Equipment Type: numeric 0.0 81.0 99.0 107.5 121.0 1204.0 258
Variable Name [84]: Post.Discharge.Home.Health.Agency Type: numeric 0.0 513.0 695.0 699.8 853.8 3200.0 258
Variable Name [85]: Post.Discharge.Hospice Type: numeric 0.0 71.0 109.0 120.9 156.0 825.0 258
Variable Name [86]: Post.Discharge.Inpatient Type: numeric 0 1642 2114 2325 2800 11160 258
Variable Name [87]: Post.Discharge.Outpatient Type: numeric 11.0 454.2 568.5 587.1 689.0 5685.0 258
Variable Name [88]: Post.Discharge.Skilled.Nursing.Facility Type: numeric 0 2164 2848 2882 3592 11430 258
Variable Name [89]: Total.Post.Discharge Type: numeric 699 6636 7630 7618 8589 19650 258
Variable Name [90]: Pre.Admission.Carrier Type: numeric 0.0 109.0 143.0 145.4 178.0 751.0 258
Variable Name [91]:
Pre.Admission.Durable.Medical.Equipment Type: numeric 0.000 6.000 8.000 9.415 11.000 186 258
Variable Name [92]: Pre.Admission.Home.Health.Agency Type: numeric 0.00 5.00 11.00 13.34 18.00 150.00 258
Variable Name [93]: Pre.Admission.Hospice Type: numeric 0.000 0.000 0.000 1.009 1.000 68.000 258

Variable Name [94]: Pre.Admission.Inpatient Type: numeric 0.0 0.0 1.0 4.6 6.0 147.0 258
Variable Name [95]: Pre.Admission.Outpatient Type: numeric 0.00 27.00 41.00 58.64 68.00 1240 258
Variable Name [96]: Pre.Admission.Skilled.Nursing.Facility Type: numeric 0.000 1.000 2.000 2.387 3.000 43.000 258
Variable Name [97]: Pre.Admission Type: numeric 1.0 185.0 229.0 234.8 278.0 2001.0 258
Variable Name [98]: During.Hospitalization.Carrier Type: numeric 5 1246 1662 1610 1961 5984 258
Variable Name [99]:
During.Hospitalization.Durable.Medical.Equipment Type: numeric 0.00 14.00 20.00 24.91 28.00 364.00 258
Variable Name [100]: During.Hospitalization.Inpatient Type: numeric 3776 6876 7845 8017 8870 26240 258
Variable Name [101]: During.Hospitalization.Total Type: numeric 3995 8248 9544 9652 10810 28620 258
Variable Name [102]: Overall.Spending Type: numeric 6195 15630 17720 17500 19440 33540 258
Variable Name [103]: Total.Spend.Ratio Type: numeric 0.53 0.9300 0.9800 0.9813 1.0300 1.6600 252
Variable Name [104]: Teaching.Status Type: character 3444 character character
Variable Name [105]: Teaching.Status.Factor Type: factor 2311 757 266 110
Variable Name [106]: READM.30.HF.HRRP.Excess Type: numeric 0 0.0000 0.0000 0.4355 1.0000 1.0000 105
Variable Name [107]: READM.30.PN.HRRP.Excess Type: numeric 0.000 0.000 0.000 0.425 1.000 1.000 105
Variable Name [108]: READM.30.AMI.HRRP.Excess Type: numeric 0 0.0000 0.0000 0.3258 1.0000 1.0000 105
Variable Name [109]: READM.30.COPD.HRRP.Excess Type: numeric 0 0.0000 0.0000 0.4241 1.0000 1.0000 105
Variable Name [110]: READM.30.HIP.KNEE.HRRP.Excess Type: numeric 0 0.0000 0.0000 0.3747 1.0000 1.0000 105
Variable Name [111]: Total.Spend.Excess Type: numeric 0 0.0000 0.0000 0.3819 1.0000 1.0000 252
Variable Name [112]: Avg.Standard.Covered.Charges Type: numeric 77.76 88.74 94.37 97.70 102.80 196.80 162
Variable Name [113]: Avg.Standard.Total.Payments Type: numeric 76.93 90.43 95.16 99.48 103.90 247.10 162
Variable Name [114]: Avg.Standard.Medicare.Payments Type: numeric 78.73 90.11 95.16 99.76 104.30 252.50 162
Variable Name [115]: Readmission.Rate.ZScore Type: numeric -4.214 -0.5883 -0.05507 0.00000 0.58480 4.85000 148
Variable Name [116]: Standard.Readmission.Rate Type: numeric 36.79 91.18 99.17 100.00 108.80 172.80 148
Variable Name [117]: Overall.Spending.ZScore Type: numeric -3.651 -0.6049 0.06876 0.00000 0.62380 5.17600 258
Variable Name [118]: Standard.Overall.Spending Type: numeric 45.24 90.93 101.00 100.00 109.40 177.60 258
Variable Name [119]: Quadrant.Category Type: character 3444 character character
Variable Name [121]: Standard.Overall.Value Type: numeric 63.30 92.62 99.19 99.97 106.30 138.90 283
Variable Name [122]: Overall.Value.ZScore Type: numeric -2.596 -0.4232 0.05399 0.00206 0.49180 2.44700 283
Variable Name [123]: Overall.Value.Percentile Type: numeric 0.00471 0.3361 0.5215 0.5053 0.6886 0.9928 283
Variable Name [124]: Overall.Value.Category Type: character 3444 character character
Variable Name [126]: Value.Quadrant.Category Type: character 3444 character character

Appendix C
R Calls
Below you will find the calls and statistical formulations used in the generation of the
models for Team HADOC in R studio. Please note that lm() is an R function used in the fitting
of linear regression models, and specifies the form of the statistical model to be fit. The response
variables are to the left of the tilde, and the predictor variables are listed on the right of the tilde.
The tilde is read as “is modeled as a function of” according to Chicago Booth.
Call for Model D
Call:
lm(formula = Standard.Overall.Value ~ During.Hospitalization.Carrier +
Post.Discharge.Inpatient + Post.Discharge.Skilled.Nursing.Facility +
Post.Discharge.Hospice, data = MF.complete)
Residuals:
Min 1Q Median 3Q Max
-36.876 -4.048 0.457 4.660 29.856
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.271e+02 6.657e-01 190.875 < 2e-16 ***
During.Hospitalization.Carrier -8.462e-03 2.629e-04 -32.189 < 2e-16 ***
Post.Discharge.Inpatient -3.878e-03 1.321e-04 -29.347 < 2e-16 ***
Post.Discharge.Skilled.Nursing.Facility -1.719e-03 1.213e-04 -14.167 < 2e-16 ***
Post.Discharge.Hospice 4.619e-03 1.702e-03 2.715 0.00667 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 7.287 on 3075 degrees of freedom
Multiple R-squared: 0.4793, Adjusted R-squared: 0.4786
F-statistic: 707.6 on 4 and 3075 DF, p-value: < 2.2e-16
Call for Model E
Call:
lm(formula = Readmission.Rate ~ During.Hospitalization.Carrier +
Post.Discharge.Inpatient + Post.Discharge.Skilled.Nursing.Facility +
Post.Discharge.Hospice, data = MF.complete)
Residuals:
-3.8265 -0.5942 -0.0584 0.5106 4.4364

Coefficients:
(Intercept) 1.511e+01 8.452e-02 178.759 < 2e-16 ***
During.Hospitalization.Carrier -1.748e-04 3.338e-05 -5.238 1.73e-07 ***
Post.Discharge.Inpatient 1.789e-04 1.678e-05 10.666 < 2e-16 ***
Post.Discharge.Skilled.Nursing.Facility 2.349e-05 1.541e-05 1.525 0.12741
Post.Discharge.Hospice -5.624e-04 2.160e-04 -2.603 0.00928 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9252 on 3075 degrees of freedom
Multiple R-squared: 0.0383, Adjusted R-squared: 0.03705
F-statistic: 30.61 on 4 and 3075 DF, p-value: < 2.2e-16
Call for Model F
Call:
lm(formula = Standard.Overall.Value ~ Post.Discharge.Carrier +
During.Hospitalization.Carrier + Post.Discharge.Inpatient +
During.Hospitalization.Inpatient + Avg.Standard.Total.Payments +
Avg.Standard.Medicare.Payments + Post.Discharge.Skilled.Nursing.Facility +
Post.Discharge.Durable.Medical.Equipment + READM.30.HF.HRRP.Discharges +
Avg.Standard.Covered.Charges + Post.Discharge.Outpatient +
Post.Discharge.Hospice + READM.30.COPD.HRRP.Discharges +
READM.30.AMI.HRRP.Discharges + During.Hospitalization.Durable.Medical.Equipment +
Post.Discharge.Home.Health.Agency + Pre.Admission.Carrier +
Pre.Admission.Outpatient + READM.30.PN.HRRP.Discharges +
Pre.Admission.Home.Health.Agency + Pre.Admission.Skilled.Nursing.Facility +
READM.30.HIP.KNEE.HRRP.Discharges + Pre.Admission.Durable.Medical.Equipment +
Pre.Admission.Inpatient + Pre.Admission.Hospice, data = MF.complete)
Residuals:
-34.160 -3.902 0.178 4.484 28.830
Coefficients:
(Intercept) 1.494e+02 1.466e+00 101.882 < 2e-16 ***
Post.Discharge.Carrier -8.011e-03 8.587e-04 -9.329 < 2e-16 ***
During.Hospitalization.Carrier -3.216e-03 5.532e-04 -5.814 6.71e-09 ***
Post.Discharge.Inpatient -2.105e-03 1.599e-04 -13.161 < 2e-16 ***
During.Hospitalization.Inpatient -1.225e-03 1.508e-04 -8.123 6.53e-16 ***
Avg.Standard.Total.Payments 2.047e-03 3.296e-02 0.062 0.950489
Avg.Standard.Medicare.Payments -1.206e-01 3.138e-02 -3.843 0.000124 ***
Post.Discharge.Skilled.Nursing.Facility -1.736e-03 1.289e-04 -13.465 < 2e-16 ***

TeamHADOC_PRED498_SEC59_FA2015_PP_FinalPaper

TeamHADOC_PRED498_SEC59_FA2015_PP_FinalPaper

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