FRAMEWORK FOR COMPUTER AIDED ANALYSIS OF MEDICAL

PROTOCOLS IN A HOSPITAL

Rene Schult, Pawel Matuszyk and Myra Spiliopoulou

Otto-von-Guericke-University, Universit

atsplatz 2, D-39106 Magdeburg, Germany

Keywords:

Knowledge discovery from medical protocols, Knowledge discovery from anesthesia protocols, Knowledge

discovery, Data mining, Healthcare information systems.

Abstract:

We study the potential of analyzing medical protocols with data mining methods for resource planing.

Background. Medical protocols can be exploited in several resource planing applications, such as optimizing

occupancy of surgery rooms or scheduling teams for surgery operations. Literature has identiﬁed many vari-

ables that can be used to predict resource demand; some of them can be extracted from medical protocols.

Contribution. We propose a high-level framework for knowledge discovery from medical protocols, and

present a ﬁrst instantiation in a German hospital. We report on the ﬁndings of this instantiation for the task of

predicting surgical room occupancy time.

1 INTRODUCTION

Hospitals are increasingly facing the demand for ef-

ﬁcient resource planing, not least in response to eco-

nomic recession and demographic evolution. Of par-

ticular interest is the efﬁcient management of re-

sources needed for expensive types of treatment, such

as intensive care, and of resources with high demand,

such as surgical rooms. Eijkemans et al. point out that

more than 60% of hospital patients undergo some sur-

gical treatment (Eijkemans et al., 2010). So, there is

need for methods for predicting and optimizing occu-

pancy of surgical rooms and intensive care units.

Medical protocols encompass information that

can be used to improve room planing. Eijkemans et

al. have identiﬁed several predictive variables for ”op-

eration time” (Eijkemans et al., 2010); some of these

variables are routinely recorded in anesthesia proto-

cols. In this study, we consider these protocols for

the prediction of surgery room occupancy time (”SRO

time”), which we deﬁne as the elapsed time between

the entry of the patient to the operation room until

the exit moment. This is equivalent to ”operation

time” in (Eijkemans et al., 2010), but we pertain to

the more explicit ”SRO time”, because in some hos-

pitals (including the one we studied) patients occupy

the surgery room until they wake up from anesthesia.

Predictive variables can serve as aid to resource

planers. However, the variables recorded vary among

hospitals. Recording all desirable variables may re-

quire process redesign and thus incur additional costs.

Hence, it is necessary to exploit the predictive power

of available variables to the largest possible extent.

To this purpose, we propose a lightweight frame-

work for knowledge discovery from medical proto-

cols and report on its use for prediction of resource

demand. The overarching idea is that the frame-

work should allow a reporting or prediction task to

be plugged into existing processes, without requiring

process modiﬁcations nor additional activities from

the medical staff. We report on a ﬁrst instantiation

of our framework in a hospital for the analysis of in-

tensive care unit protocols and anesthesia protocols.

We show how knowledge discovery from anesthesia

protocols can lead to better prediction of SRO time;

the full report is in (Schult et al., 2011).

In section 2 we discuss related work. In section

3, we describe our framework at an abstract level; in

section 4 we present its instantiation in a hospital on

two types of medical protocols. Findings on the pre-

diction SRO time from anesthesia protocols are sum-

marized in section 5. Section 6 concludes our study

with lessons learned and planed next steps.

2 RELATED WORK

The importance of information technology in the

health care industry is reﬂected in increasing invest-

ments in appropriate IT systems (Wilson and Tulu,

225

Schult R., Matuszyk P. and Spiliopoulou M..

FRAMEWORK FOR COMPUTER AIDED ANALYSIS OF MEDICAL PROTOCOLS IN A HOSPITAL.

DOI: 10.5220/0003776702250230

In Proceedings of the International Conference on Health Informatics (HEALTHINF-2012), pages 225-230

ISBN: 978-989-8425-88-1

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

2010). Avison and Young point out that decision sup-

port systems are one important application in health

care information systems (Avison and Young, 2007),

while Combi et al. stress the importance of times-

tamped data for reasoning, e.g. for clinical diagnosis

and for devising care plans (Combi et al., 2010).

Reasoning, prediction and other forms of decision

support require generic frameworks that allow for the

particularities of each hospital. For example, con-

sider prediction of surgery room occupancy: Dex-

ter et al. report that the average duration of a given

surgery between the second-fastest and the second-

slowest clinic they investigated may differ by up to

50% (Dexter et al., 2006). This implies that predictive

models must be learned for each hospital, on the data

recorded in this hospital. Accordingly, we propose a

framework at a high level of abstraction, and we show

how its instantiation in a German hospital lead to the

exploitation of medical protocols for prediction.

3 FRAMEWORK FOR MEDICAL

PROTOCOLS IN DECISION

SUPPORT

We propose a framework for decision support on the

basis of medical protocols. The main purpose of these

protocols is to store all medical activities that refer to

a patient of a hospital. They are essential for the pa-

tient’s treatment, but also for accounting and billing,

for resource management and planing, and for audit-

ing. They can also be used for scientiﬁc research,

studies on new treatments and medication, and for the

analysis and optimization of internal processes.

In our framework, we consider medical protocols

for decision support in resource management, and an-

ticipate two core tasks - reporting on resource use, and

predicting resource use. For prediction we focus on

(supervised) data mining methods. All tasks we an-

ticipate are depicted in Figure 1 and described below.

Figure 1: Framework for reporting and analyzing medical

protocols for decision support.

There are different types of medical protocols

that can be used as input to our framework, such

as anesthesia protocols or intensive care unit proto-

cols; they are compiled by different members of the

medical staff, are incorporated in different processes,

and intended for different recipients. This affects the

contents of the protocols, the number of variables

recorded and the format used. Some protocols have

the form of a single record per patient, while oth-

ers adhere to the entity-attribute-value model (Stead

et al., 1983) and consist of several records per patient

– one per variable of interest for this patient. These

differences inﬂuence the prediction task, and must be

dealt with during data preparation.

Data Preparation Task. This task involves algo-

rithms that prepare the data for reporting and for data

mining. Conventional data preparation tasks include

ﬁnding and handling missing values and errors in the

data, detecting correlations between variables, and

determining the target variable for the subsequent pre-

diction task, depending on the problem at hand. There

are many statistical tools and also mining algorithms

available for such purposes; in our experiment (Sec-

tion 5) we report on those we used for our instantia-

tion to predict SRO time.

Less conventional data preparation is needed to

transfer data from the entity-attribute-value model to

a format that can be used by mining algorithms. We

elaborate more on this issue in Section 4.

Reporting Task. This task involves utilities for data

querying and summarization, as provided convention-

ally with a database management system or data ware-

house. The concrete information to be reported de-

pends on the objective of decision support. For ex-

ample, optimizing surgery room occupancy (SRO) re-

quires an overview of SRO times for different vari-

ables, such as type of surgical treatment, patient age

etc. Relevant variables are listed in (Eijkemans et al.,

2010), but the contents of the output report Statistics

depend obviously on the variables recorded in the pro-

tocols used. In our instantiation (Section 4), we used

anesthesia protocols.

Prediction Task. This task involves data mining al-

gorithms, as provided by commercial suites or open

source tools (free for research purposes). Prediction

can run independently of the reporting task, but often

reporting precedes prediction: some reports can pro-

vide insights on predictive variables.

The algorithms used depend obviously on the con-

crete objective for decision support. In our instanti-

ation, we wanted to predict discrete SRO time slots

(”bins”) rather than exact SRO time, since surgery

rooms are rather occupied in time slots than up to the

minute. is more interessting as the prediction of the

concrete time. For the prediction of values of a con-

tinuous variable, regression methods should be used,

while the prediction of discrete values requires classi-

ﬁcation algorithms.

HEALTHINF 2012 - International Conference on Health Informatics

226

The prediction task involves speciﬁcation of the

target variable and of appropriate evaluation criteria.

Prediction requires training, tuning and comparing

several learners, before a learner (or an ensemble) is

chosen to be used for the prediction over unknown

data. In our experiment (cf. Section 5), we compared

several classiﬁers, but we also compared data prepara-

tion algorithms, because they turned to inﬂuence clas-

siﬁer performance.

Integration and Comparison. This task involves

placing the report (from the reporting task) and the

results of the prediction task together, including vi-

sualizations. The tools needed here are usually part

of the suites appropriate for the Prediction, resp. the

Reporting task. Integration serves foremostly the jux-

taposition of ﬁndings acquired from reports via sim-

ple statistics and querying, and those acquired by ma-

chine learning. The ﬁnal report serves as basis for a

human decision maker for planing, or as input for a

simulation tool that may consider different resource

planing scenaria. In the long term, comparison also

concerns the juxtaposition of a predictor learned some

time back with newer data; changes in healthcare pro-

cesses or external factors may require re-learning of

predictive models.

In the next two sections, we discuss an instantia-

tion of our framework in a German hospital for inten-

sive care unit protocols and anesthesia protocols, and

summarize our insights from applying our framework

to predict SRO time.

4 INSTANTIATION OF THE

FRAMEWORK IN A GERMAN

HOSPITAL

We present an instantiation of our framework for

knowledge discovery in a German hospital. We study

two types of medical protocols recorded from 2007

till 2009, namely intensive care unit protocols and

anesthesia protocols. As pointed out in section 3, such

medical protocols are typical inputs to our framework.

We ﬁrst describe brieﬂy the challenges and potentials

of using intensive care unit protocols for knowledge

discovery. Then, we focus on anesthesia protocols,

which we use for the prediction of surgery room oc-

cupancy time (SRO time, cf. Section 1). Our experi-

ment on these protocols is described section 5.

4.1 Intensive Care Unit Protocols

An intensive care unit protocol contains data on the

treatment of a patient in an intensive care unit. Such

data include diagnosis and medication. Special em-

phasis is put on the patient’s vital signs, e.g. body

temperature, pulse and blood pressure, which have to

be monitored constantly.

The data stored in intensive care unit protocols

are very complex: depending on the patient and the

treatment, different variables must be stored. Given

the limitations of database systems with respect to

the maximum number of columns in a table, Stead et

al. proposed to use the entity-attribute-value model

(Stead et al., 1983), where the entity is the patient

identiﬁer, and (attribute, value)-pairs contain the spe-

ciﬁc variables and values to be stored for this patient.

This model was used for the intensive care unit proto-

cols in our instantiation, whereby an identiﬁer and a

timestamp was added to each record.

The entity-attribute-value model is not appropri-

ate for data mining. The reason is that a data mining

algorithm requires that all values belonging to one in-

stance (here: one patient of the intensive care unit) be

in one record, so that the algorithm accesses and anal-

yses all attributes of the record together. In contrast,

under the entity-attribute-value model the values be-

longing to one patient are spread over time and stored

separately, as if they belonged to different stays and

treatments of the patient. Hence, the data of each in-

tensive care unit protocol must be collected and inte-

grated into a single record. However, since there is a

large number of possible attributes but only a few are

recorded for each speciﬁc patient, the density of infor-

mation for each patient could be too low for learning.

One solution to this problem could be the follow-

ing: group protocols for which the same attributes

have been recorded (preparation task), perform re-

porting and knowledge discovery on resource de-

mand, such as bed occupation or drug utilization,

for each group separately (reporting task / prediction

task), and then integrate the ﬁndings of the groups

into a report (integration and comparison task).

4.2 Anesthesia Protocols

An anesthesia protocol contains an exact description

of all anesthetic activities performed during a surgical

treatment. Among the data contained in such a proto-

col are involved personnel, important time points (e.g.

time point of the incision and of the end of a surgery),

and data about medication.

In the hospital of our study, these data were

recorded by an anesthetist during the surgery, us-

ing the Anesthesia Information Management System

(AIMS) NarkoData. NarkoData contains all data as-

sociated to anesthesia during the whole anesthesia

process, including drugs, laboratory results, relevant

FRAMEWORK FOR COMPUTER AIDED ANALYSIS OF MEDICAL PROTOCOLS IN A HOSPITAL

227

vital signs, as well as data on the attributes speciﬁed

by the German Society of Anesthesiology and Inten-

sive Care Medicine

(DGAI, 1993). NarkoData also

contains data from the hospital information system,

including data on patients and medical staff. Patient’s

attributes are age, weight and body size, disease ac-

cording to the ICD Classiﬁcation (WHO, 2011), phys-

ical status according to the ASA-Classiﬁcation, and

type of anesthesia. Information on medical staff is

limited to the identiﬁers of surgeons and anesthesists.

This allows us to distinguish among staff members

without disclosing personal information.

The time points recorded in the anesthesia proto-

cols are very important: they can be used to predict

the duration of future, similar surgical treatments. In

the next section, we present the ﬁndings of the frame-

work’s instantiation in the German hospital for the

prediction of SRO time using anesthesia protocols.

We discuss the concrete activities of

data preparation (Section 5.1) and learning (Sec-

tion 5.2), including results and lessons learned. Sec-

tion 5 is a summary of (Schult et al., 2011), where all

details can be found.

5 KNOWLEDGE DISCOVERY

EXPERIMENT ON

ANESTHESIA PROTOCOLS

The goal of knowledge discovery from anesthesia

protocols in the hospital under study was to learn a

model that predicts the SRO time of future surgery

treatments better than the current baseline. This in-

volved an instantiation of the data preparation task

and of the prediction task (cf. Figure 1). In our

experiment, we consider three discretization meth-

ods for data preparation, and four classiﬁcation algo-

rithms for prediction, and we compare the quality of

the models learned by the twelve combinations.

Our target variable is a discretized version of SRO

time. As described in section 1, we deﬁne ”SRO time”

as the elapsed time between entry and exit of the pa-

tient to/from the surgery room. We thus cover hospi-

tals that do not have a separate room where patients

stay after surgery until they wake up.

We discretize SRO time for learning, because

room occupancy plans deliver time slots (equiv. bins)

rather than exact values. The size of the bin affects

prediction, so we experimented with different binning

methods in the data preparation task described below.

DGAI: Deutsche Gesellschaft f

ur An

asthesiologie und

Intensivmedizin

For the evaluation, we compare our models to a

baseline predictor ICDavg: for each class of surgery

according to the ICD classiﬁcation (WHO, 2011), IC-

Davg ﬁnds all protocols refering to treatments of this

class, adds their SRO times and computes the aver-

age. Then, for each anesthesia protocol in the testset,

ICDavg identiﬁes the ICD class of the treatment and

returns the corresponding SRO time average.

To compare our models to ICDavg, we map back

the predicted bin of SRO time to the mid value of the

bin (e.g. a bin of 90 min is mapped to 45 min). Then,

we deﬁne a function that computes the Cumulation of

Absolute Differences between true and predicted SRO

time (SROCD) for the whole period of study. The

lower the SROCD, the higher is the model’s quality.

5.1 Data Preparation

The data preparation task in our instantiation for SRO

time prediction involved following activities: (a) in-

corporation of the surgeonID in each record, (b) com-

putation of the SRO time per record and (c) discretiza-

tion of the SRO time into a ﬁxed set of intervals/bins,

so that the SRO time bin becomes the label to be pre-

dicted in the prediction task.

The incorporation of the surgeonID into the anes-

thesia protocols is important because this variable is

predictive (Eijkemans et al., 2010). However, some

surgical treatments involve more than one surgeon,

so that the incorporation of the identiﬁer transformed

one protocol (from the originally 33,862 anesthesia

protocols) into multiple records, whenever multiple

surgeons were participating. This was not a prob-

lem for our experiment, because the duplicates were

considered both by the baseline and by each learner.

However, in a real scenario, a more elaborate ap-

proach is needed for the incorporation of multiple

identiﬁers of surgeons into a single record.

The computation of the SRO time of each pro-

tocol is performed by using the timestamps and is

fairly straightforward. For discretization, one may

provide the target number of bins as input, or con-

sider methods that both estimate this target number

and do the binning. Since there are cases where the

latter type of methods is not of advantage (see sec-

tion 5.2), we propose following approach, under the

assumption of representative data, to specify the tar-

get number of bins: generate bins for different input

numbers, learn a classiﬁer for each number of bins,

compute the SROCD, and identify the moment of sat-

uration of the SROCD curve.

To test this approach we have experimented in

(Schult et al., 2011) with the discretization methods

(i) Equal Width Interval Binning (EWIB) that parti-

HEALTHINF 2012 - International Conference on Health Informatics

228

tions the SRO times in the anesthesia protocols into

bins of equal size, and (ii) K-Means that groups sim-

ilar SRO times into K clusters, whereby each group

becomes a bin. Unlike EWIB, K-Means builds bins

that are not necessarily of equal width.

Figure 2 depicts the SROCD curve of a J4.8 de-

cision tree classiﬁer upon bins computed with EWIB:

the curve does converge. The saturation is on 50 bins,

the same value was found when using K-Means in-

stead of EWIB. Hence, we can use this experimental

approach to determine the number of bins, provided

that the data set is representative. Anesthesia proto-

cols are recorded anyway for each surgical treatment,

so representative samples can be drawn from them.

●

2 5 10 20 50 100 200

1500000 2000000 2500000

Number of classes

SROCD (in minutes)

Figure 2: SROCD (in minutes) of a decision tree learner

(J4.8) for K = 2, . . . , 200 bins; the contents of a bin for each

value of K was computed with EWIB.

5.2 Prediction

In our instantiation, prediction translates into a clas-

siﬁcation task, because the target variable (SRO time)

has been discretized. It is essential to deﬁne a baseline

and to study how different learners behave in compar-

ison to this baseline. Then, for the operative task of

prediction on new, unlabeled data, the best model or

an ensemble of learned models should be used.

As baseline we used the ICDavg described at the

beginning of this section: it computed an expected ac-

cumulated SRO time of 1,279,567 minutes. We com-

pared it to Na

ıve Bayes, to the ID3 decision tree clas-

siﬁer of (Quinlan, 1986), to the J4.8 Java implemen-

tation (Witten and Eibe, 2005) of C4.5 (a successor of

ID3), and to a random forest (Ho, 1995) - an ensemble

of decision trees. For all learners we performed 10-

fold cross validation. For binning, we used K-Means

and EWIB (K = 50 bins), and the Tree-Based Unsu-

pervised Bin Estimator TUBE of (Schmidberger and

Eibe, 2005), which estimates the number of bins. Pa-

rameter settings can be found in (Schult et al., 2011).

The lowest SROCD value (882,513 minutes) was

achieved by ID3 after binning with K-Means. How-

ever, ID3 classiﬁers abstain from classifying some

records in the test set, hence it is inferior to the learn-

ers build by the other algorithms, which assigns la-

bels to all records. The overall best performance is

achieved by Random Forest with 15 trees (910,383

minutes), and the second best by J4.8 (1,073,231 min-

utes), in both cases after binning with K-Means. The

single tree of J4.8 improves the baseline by 16.2%.

An overview of the results is given in Figure 3.

The horizontal line is the SROCD value achieved by

the baseline ICDavg; of interest are only predictors

that achieve lower SROCD, i.e. improve the baseline.

For each learner, we depict the performance achieved

by each binning method. We see that TUBE leads

to worst performance, K-Means to best performance,

EWIB being only slightly inferior to it.

SROCD (in minutes)

Figure 3: SROCD values for each classiﬁcation algorithm

(X-axis) combined with each binning method (legend); the

baseline ICDavg is depicted as horizontal line. Lower val-

ues are better, and only values below the baseline corre-

spond to an improvement in predictive power.

In classiﬁcation tasks, it is usual to evaluate on ac-

curacy. In Table 1, we juxtapose accuracy to SROCD

for Na

ıve Bayes (worst SROCD) and J4.8 (second

best SROCD), considering each binning method. The

juxtaposition shows that accuracy is inappropriate for

the task at hand, as it behaves contrary to SROCD. In

particular, we see that both learners achieve best ac-

curacy under TUBE, but much poorer SROCD values

than under EWIB or K-Means. In contrast, the accu-

racy under EWIB and K-Means is very low.

This is an artifact that may lead the decision

maker to wrong conclusions, so we explain it here:

EWIB and K-Means produced 50 bins, distributing

records evenly among them. TUBE produced 99 bins,

but most of the records were placed in only 3 of

them. Hence, under TUBE, the classiﬁers essentially

learned to distinguish among three labels/bins. The

FRAMEWORK FOR COMPUTER AIDED ANALYSIS OF MEDICAL PROTOCOLS IN A HOSPITAL

229

Table 1: Impact of binning method on the performance of the classiﬁers learned by Na

ıve Bayes (NB) and J4.8: performance

is measured as accuracy (higher values are better) and SROCD (lower values are better). Accuracy is an ill choice for the task

at hand, because it shows bias to the number of labels and to the distribution of the data among the labels.

Binning method # bins Accuracy (in%) SROCD (in min)

NB J4.8 NB J4.8

EWIB 50 21.23 41.11 1,488,230 1,120,729

K-Means 50 14.39 35.41 1,639,846 1,073,231

TUBE 3+96 82.12 88.01 4,099,064 4,111,336

prior probability of a miss (wrong label assignment)

is higher if there are 50 labels than if there are only

three. Accuracy is sensitive to the number of labels,

so it is an ill choice if classiﬁers are learned with dif-

ferent numbers of labels or with a strong bias towards

only a few labels.

Summarizing, the instantiation of the prediction

task for the hospital resulted in predictors that im-

proved the baseline. Among the lessons learned are

the impact of discretization on the learners and the

importance of selecting a proper evaluation measure.

6 CONCLUSIONS

We presented a high-level framework for knowledge

discovery from medical protocols, and its instantia-

tion in a German hospital for the prediction of surgi-

cal room occupancy time (SRO time). Such data are

primarily recorded for medical purposes , but can be

used to support planing decisions, too, provided they

are appropriately prepared and analyzed.

In the instantiation of our framework in a German

hospital we studied intensive care unit protocols and

anesthesia protocols. Instantiation on the former is

still under data preparation, since the intensive care

units’ data were in a format not yet appropriate for

data mining. Anesthesia protocols have been success-

fully analyzed after a preprocessing task that involved

computation and discretization of the target variable

(SRO time). We reported on what steps should take

place during preprocessing and analysis, how differ-

ent algorithms can affect the predicting power of the

learned models, and how they should be compared.

Next steps include the reﬁnement of our frame-

work towards speciﬁc activities for decision sup-

port tasks, and instantiations for knowledge discov-

ery from other types of medical protocols, foremostly

from intensive care unit protocols.

REFERENCES

Avison, D. and Young, T. (2007). Time to rethink

health care and ICT? Communications of the ACM,

50(6):69–74.

Combi, C., Keravnou-Papailiou, E., and Shahar, Y. (2010).

Temporal Information Systems in Medicine. Springer.

Dexter, F., Davis, M., Halbeis, C. E., Marjamaa, R., Marty,

J., McIntosh, C., Nakata, Y., Thenuwara, K. N.,

Sawa, T., and Vigoda, M. (2006). Mean operating

room times differ by 50% among hospitals in different

countries for laparoscopic cholecystectomy and lung

lobectomy. Journal of Anesthesia (2006) 20:319–322.

DGAI (1993). Qualit

atssicherung und Datenverarbeitung in

der An

asthesie. Kerndatensatz Qualit

atssicherung in

der An

asthesie. An

asth Intensivmed, 34:331–335.

Eijkemans, M. J. C., van Houdenhoven, M., Nguyen, T.,

Boersma, E., Steyerberg, E. W., and Kazemier, G.

(2010). Predicting the unpredictable: A new predic-

tion model for operating room times using individual

characteristics and the surgeon’s estimate. Anesthesi-

ology 2010; 112:41–9.

Ho, T. K. (1995). Random decision forests. 3rd Int’l Conf.

on Document Analysis and Recognition.

Quinlan, J. (1986). Induction of decision trees. Machine

Learning 1: 81-106, 1986.

Schmidberger, G. and Eibe, F. (2005). Unsupervised dis-

cretization using tree-based density estimation. Lec-

ture Notes in Computer Science, Volume 3721/2005,

240-251.

Schult, R., Matuszyk, P., and Spiliopoulou, M. (2011). Pre-

diction of surgery duration using empirical anesthe-

sia protocols. In The First International Workshop on

Knowledge Discovery in Health Care and Medicine

(KDHCM 2011), pages 66 – 77.

Stead, W., Hammond, W., and Straube, M. (1983). A chart-

less record - is it adequate? Journal of Medicine Sys-

tems, 7:103 – 109.

WHO (2011). World health organization: International

classiﬁcation of diseases (ICD). http://www.who.int/

classiﬁcations/icd/en/.

Wilson, E. V. and Tulu, B. (2010). The Rise of a Health-

IT Academic Focus. Communications of the ACM,

53(5):147–150.

Witten, I. H. and Eibe, F. (2005). Data mining : practical

machine learning tools and techniques. Amsterdam:

Elsevier; San Francisco, CA: Morgan Kaufmann.

HEALTHINF 2012 - International Conference on Health Informatics

230