Creating equal cost groups for trauma

patients in hospitals in Israel

The Gertner Institute, Sheba Medical Center, Israel

This research was carried out with:

Michal IvankovskyProfessor Laurence Freedman

Dr. Amir ShmueliDr. Kobi Peleg

Can have several complex injuries

Often a relatively large number of diagnoses/patient

A relatively large number of injured body areas

Can have high injury severity

High costs

An increased need for expensive resources

Trauma Patients

Classification and severity of injuries◦ NISS- New Injury Severity Score

The NISS sums the severity scores for the three most severe injuries, regardless of body region

Trauma Patients

The Israel National Trauma Registry is maintained by the Israel National Center for Trauma and Emergency Medicine Research

It contains data on hospitalized patients at 10 trauma centers in Israel- all 6 level 1 Trauma Centers in the country and 4 regional trauma centers

◦ A level 1 Trauma Center provides total care for every aspect of injury, and conducts research.

◦ A level 2 Trauma Center also provides comprehensive care, but may not have all the specialties of a level 1 center, and is also not committed to conducting research.

The Israel Trauma Registry

Over 200 data fields are included in the registry including demographic information about the patients, details on the injury which includes diagnoses (up to 20 per patient), severity indicators, details on the external causes of the injury, treatment, length of hospital stay and outcome.

The Registry, which has been maintained since 1997, accumulates approximately 20,000 records per year.

The Israel Trauma Registry

Israeli hospitals are currently compensated for trauma patients by some function of the duration of hospital stay, according to the average per diem rate, and not injury severity

Trauma patients incur much higher costs: duration of hospital stay does not accurately reflect these costs

Is preference given to patients whose treatment will be less expensive?

Financial Compensation for Trauma Patients

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Average Length of Hospital Stay of Trauma Patients compared to other patients in 2002, in

different units

A separate fairer classification system for trauma patients to be used as a new management tool

Setting up of “equal cost groups” based on length of stay, but also taking other variables into account e.g. those dealing with resource diagnoses

Ability to forecast costs for trauma patients

Solution- A Prospective Payment System?

Variables used to create the groups must be based on data collected routinely by hospitals

The total number of groups should be “reasonable” and together should include all the patients

Each group should include patients with a similar resource utilization

Each group has to be logical from a clinical point of view

Principles of Equal Cost Groups

Prospective payment systems (PPS’s) for hospital reimbursement have been established in many countries. Integral to the PPS is the use of pre-defined diagnoses and diagnosis related groups (DRG’s)

But- most PPS’s based on DRG’s were not created specifically for trauma cases

Other PPS models

A system which classifies hospital cases into 1 of many groups (DRG’s) developed as part of a PPS. Based on ICD diagnoses, procedures, age, sex and the presence of complications or comorbidities

Inappropriate for severe and complex trauma cases?

Diagnosis Related Groups (DRG’s)

Used Length of Hospital Stay as the measure of resource utilization

Made definitions for multiple trauma patients based on the primary and secondary diagnoses

-or required two or more substantial injuries in different body systems

-or used the full clinical profile of the patient

Existing PPS Models

Logical choice- creation of homogenous groups

No problems with missing variables

Handles large data sets with ease

Easily interpretable=> convenient management tool

Once trained, can be used for forecasting?

Using regression trees to build equal payment groups

Usual measure in these models is length of stay (LOS) in hospital

We constructed a new measure- “cost days” as a sum of two components:

◦ (1) number of hospitalization days not spent in the ICU

◦ (2) 4.33 times the number of days in the ICU. The cost of a day of hospitalization in the ICU is estimated as equal on average to the cost of 4.33 days in other hospital units

In addition, we obtained 3 years of actual costs for trauma patients in 2 hospitals

Measures of resource utilization

However, the distribution of “costdays” and actual costs is highly skewed to the right- there are many lower values and far fewer higher values

In order to avoid creation of many sub-groups with high values of “costdays” or actual costs, a log transformation was used

Costdays and Actual Costs

Based on admittance data only

Divided into 6 groups; ◦ patient characteristics-demographics: age

◦ Characteristics of the trauma center: Level 1 or Level 2

◦ Characteristics of the injury: The severity score- NISS

The body regions which were injured

Circumstances of injury (ecode)

◦ Diagnoses of hip fracture

◦ Diagnoses of burns

Variables used to build the trees

Vital status “at discharge”

Operations

Race

Number of ICD9 codes

Variables not used to build the trees

Not readily available

For this project, real costs were evaluated for each trauma patient in 2 hospitals (one for level 1 and one level 2) for a period of 3 years

Although costs were calculated for a variety of hospital resources e.g. laboratory costs, operative costs etc., we only included the total actual cost incurred by a patient

Calculation of costs were subcontracted to an external organization

Actual Costs

Trees built for each hospital separately and together

Training set built on a random two thirds of the data set, and the learning set on the remaining third

Trees built both for actual costs and costdays

Log transformation used for dependent variables- more sensitive to lower numbers

Model Building

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Average actual costs for different resources

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Actual Cost Tree for both hospitals combined

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Actual Cost Tree for the Level 1 Hospital

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Actual Cost Tree for the level 2 Hospital

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Costdays Tree for both hospitals combined

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Costdays Tree for the Level 1 Hospital

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Costdays Tree for the Level 2 Hospital

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Comparison between trees for combined hospital models

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Comparison between trees for Level 1 hospital models

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Predictive Ability of the Level 2 models

Trees built on actual costs and costdays

For each terminal node, for both types of tree, the deviance based on actual costs is calculated;◦ Σ(log(actual cost)-log(predicted cost))^2 and these

deviances are summed over all the terminal nodes for each tree (sum of sum of squares or deviances)

This results in 2 measures- one from each type of tree

If the ratio between these 2 measures is close to 1, we claim that these 2 trees are comparable

Comparison between trees

Each set of data was resampled 200 times, and both types of tree built on new training sets

For each new pair of trees, comparability was looked at by comparing the ratio of the sum of deviances for each pair of trees tested on the new learning set

The mean ± s.d of the ratios; both hospitals combined; 1.016±0.023

◦ Level 1 Hospital; 1.025±0.028◦ Level 2 Hospital; 1.009±0.031

Assessing sampling variation

T-tests for log means

2 sample t-tests were conducted between the true means (from the training set) and the predicted means (from the learning set) at each terminal node. Since there are some schools of thought that prefer to retain the original log transformation used during analysis, t-tests were carried out using the log values of the costs. The results showed some significant values.

Predictive Ability of the models

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Predictive Ability of the models- combined trees

The true and predicted total costs at each node were calculated for the predicted trees

The predicted total “actual cost” or total “costdays” were calculated by multiplying the mean number of “actual costs” or “costdays” at each node in the regression tree by the number of trauma patients in the learning set who were classified at that node, and then summing over all the terminal nodes

The ratio of true (T) to predicted (P) total costs (T/P) was used to see how far the true costs deviated from the predicted.

Predictive Ability of the models

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Predictive Ability of the combined models

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Predictive Ability of the Level 1 models

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Predictive Ability of the Level 2 models

It is expected that deviant resource utilization in each group resulting from untypical and extreme cases, such as those with unusually short or long hospital stays, costs, transfers, expensive medical or surgical procedures, etc. will be encountered

Excluding cases which are defined as outliers, enhances the statistical homogeneity with the group

Definition- Mean ± 3 x s.d.

The Question of Outliers

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Actual Cost Tree for both hospitals combined- new means after removal of

outliers

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Actual Cost Tree for both hospitals combined- effect of removal of outliers

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Costdays Tree for both hospitals combined- new means after removal of outliers

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Costdays Tree for both hospitals combined- effect of removal of outliers

An important aspect of this project is whether the models can be used to predict future costs

To this end, 6 years of data were extracted from the trauma registry for 8 hospitals- the first 3 years (1998-2000) were used for training the model, and the following 3 years were used as learning datasets

No actual cost data is available for this dataset- costdays used as the dependent variable

Costdays Tree for 8 Hospitals

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Costdays Tree for 8 Hospitals

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Predictive ability of the model

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Predictive ability of the model

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Predictive ability of the model

Costdays is a good proxy for actual costs

The proposed models benefit from the removal of outliers

The predictive ability of models for future years must be interpreted carefully

The proposed models are a step towards establishing a more equitable prospective payment system

Conclusions