Ad 2016 IEEE International

Unlock access to all the studying documents.

View Full Document

A Platform based on Multiple Regression to Estimate

the Effect of in-Hospital Events on Total Charges

Dimitrios Zikos

Department of Health Administration

Central Michigan University

Mt. Pleasant, MI, United States

zikos1d@cmich.edu

Dhanashri Ostwal

Computer Science and Engineering Department

University of Texas at Arlington

Arlington, TX, United States

anashrivilas.ostwal@mavs.uta.edu

Abstract—Recently hospitals struggle to control the cost of care

while maintaining optimal outcomes. To respond to this challenge,

we developed an interactive web platform which utilizes a multiple

linear regression model. The user can c

reate

and furthermore

alter a clinical scenario, during a patient hospitalization to see the

dynamic prediction of total charges, via interactive sessions. The

R2value of our model is 0.655 and the standard error of the

estimate is $38,732. Predictors with high coefficient scores include

the

cardioverter implantation, mechanical venti

lation, implant of

pulsation balloon and hospital-acquired conditions such as

staphylococcus aureus septicemia. Our findings indicate that (a)

integration of predictive models into clinical decision support

systems is feasible and use of regression methods provide direct

feedback on the effect of any clinical practice to the in-hospital

charges (b) medical claims data can provide a useful estimation of

the in-hospital charges (c) hospital acquired conditions have

significant impact on the in-hospital charges.

Keywords—total charges; multiple linear regression; prediction;

decision making

I. INTRODUCTION

Hospitals

in the United States

are in a constant effort to

provide high-quality services without undergoing unneeded

procedures. There is a need of maintaining a balance between

optimal health outcomes and the cost of the provided care. Novel

practices and therapeutic methods are being introduced into the

clinical practice, hospitals purchase new equipment and capacity

to provide modern services, often with important amortization

considerations to be made during budgeting. Increased health

care costs have not necessarily led to improved outcomes.

According to the American Hospital Association, overdiagnosis

and overuse of treatments have increased health care costs with

barely any improvement in health outcomes [1]. While there is

a lot of research associating nursing and quality of care, very

little has been done on the impact that clinical and nursing

practices have, to the cost of care and the total charges of an in-

hospital stay [2].

At the same time, we recognize an unmet need for services

that provide dynamic, individualized estimations of the effect of

clinical interventions to in-hospital charges, during the clinical

practice. Such a dynamic estimation

would not only provide

insight on the projected financial burden of the hospital stay, but

it could also be used to drive decisions via the interaction of

therapists with clinical decision support systems which integrate

the aforementioned functionality.

It is not uncommon for multiple regression techniques to

model the cost as a function of covariates that are observed in

the patients. The estimated beta coefficients have been reported

to provide an estimat

ion of

the total cost for each admission case

[3]. Generalized Linear Models have also been used in the past

for the cost of care estimation [4]. In another case, researchers

utilized data from stroke patients and used DRGs, and other

hospital variables in order to construct a regression model which

explained a 61% of the cost of care variance [5]. Cost prediction

models are often driven by limitations related with the non

–

availability of features which would help explain a higher

percentage of the variability. In [6], researchers used hospital

admission information and their model could explain a rather

small ratio of the total charges variance, which was no higher

than 34%.

In the case of the Intensive Care Unit (ICU) cost estimation,

Moran et al. [7] used a combination of ICU activ

ity indices and

severity scores for cost prediction. In a similar work by

Ramianira et al. [8] researchers estimated the costs and then used

a standard linear regression model to correlate cost units and

their predictors. The study identified as importation predictors,

the patient gender and age, the admission type

(urgency/elective), ICU admission, blood transfusion, the

admission outcome (death/no death), the complexity of medical

procedures, and a risk-adjustment index. Researchers from MIT

presented an algorithmic approach to predict the cost of care [9]

by utilizing classification trees and clustering algorithms on

claims data from more than 800,000 patients. The authors of this

study stressed the limitations of using the R2value as the primary

evaluator of the prediction accuracy.

The majority of the aforementioned studies have used

regression methods to pre

dict the cost of care and have

approached the problem in a conventional statistical manner.

There are no research examples in the literature, though, of

efforts to integrate predictive models into decision support

systems which can be used by the hospital administration and

clinicians in an interactive manner, during the course of the

healthcare provision. To respond to this unmet need, we first

developed and

evaluated

a multiple linear regression model and

then we integrated the model into an interactive web interface,

which provides direct feedback to the hospital administration

and to clinicians. During the clinical care, users are presented

with an estimation of the total charges based on the selection of

their preferred attributes of clinical care. Subsequently, they can

2016 IEEE International Conference on Healthcare Informatics

DOI 10.1109/ICHI.2016.72

403

alter any attribute value to see the effect of such a change to the

cost of care, and overview a comparison of consecutive runs.

There are many levels of interest and a variety of possible use

case scenarios; The hospital administration would be provided

with a realistic snapshot of the total charges per patient as well

as per unit. Clinicians, being members of the hospital team,

would extend to the strategic goals of the hospitals since they

would have available tools assisting them to make important

cost-benefit considerations, during the clinical practice.

The contribution and importance of our study is the

introduction of an online platform, which is built around a

reasonably performing regression model, rendering the system

easy to use without any prior in-depth understanding of

statistics, and providing direct meaningful feedback

hospital

administrators and clinicians.

The paper is organized as follows: Section II describes the

data that we used for the development of our platform and the

preprocessing. Section III provides detailed information on the

training and the performance of the predictive model. Finally,

Section IV presents the architecture and functionality of the web

platform and an example use-case scenario.

II. DATA SELECTION AND PREPARATION

A. Description of the Data

B. Data Preprocessing

To facilitate the estimation of the cost of care with the use

multiple linear regression (MLR), we transformed the dataset to

a sparse data file by computing multiple binary attributes for the

unique values of the original dataset. The categories for all non-

ordinary nominal attributes were transformed to new binary

attributes, essentially describing the existence (value=1) or non-

existence (value=0) of a diagnosis, a medical procedure, or a

hospital-acquired condition (HAC), acting like a switch. These

binary attributes are going to be used as our features to predict

the total charges (dependent variable). The user will know the

impact of a change of an attribute value to the total charges, by

observing the beta coefficient of the attribute. In a linear

regression equation, the beta coefficient of any attribute is equal

to the units of change to the d

ependent variable (in our case the

total charges) when the value of that attribute increases by one

unit.

We removed from the dataset any attributes that would

normally be unavailable at the point of the decision, in a real

hospital context. The point of the decision can any time after the

admission and during the patient hospitalization. The attributes

we removed include the Diagnosis Related Groups (DRG) price,

the discharge destination, the discharge status, and all cost-

related attributes. We opted for the inclusion of the HACs since