Linking Diagnostic-Related Groups (DRGs) to their Processes by

Process Mining

Alessandro Stefanini

, Davide Aloini

, Riccardo Dulmin

and Valeria Mininno

Department of Enterprise Engineering, University of Rome Tor Vergata, Via Orazio Raimondo, Rome, Italy

Department of Energy, Systems, Territory and Construction Engineering (DESTEC), University of Pisa, Pisa, Italy

Keywords: Healthcare, Process Discovery, Process Mining, Diagnostic-Related Group (DRG), Patient-flow.

Abstract: The knowledge of patient-flow is very important for healthcare organizations, because strongly connected to

effectiveness and efficiency of resource allocation. Unfortunately, traditional approaches to process analysis

are scarcely effective and low efficient: they are very time-consuming and they may not provide an accurate

picture of healthcare processes. Process mining techniques help to overcome these problems. This paper

proposes a methodology for building a DRG related patient-flow using process mining. Findings show that it

is possible to discover the different sequences of activities associated with a DRG related process. Managerial

implications concern both process identification, analysis and improvement. A case study, based on a real

open data set, is reported.

1 INTRODUCTION

Healthcare is one of the most relevant field in every

modern society, for two main reasons: the great

interest of people for health and the meaningful

impact on the world economy. Healthcare is one of

the most important economic sector in the developed

countries: “OECD Health Statistics 2014” shows that

the Health spending accounted for 9.3% of GDP in

the OECD countries. Due to the impact of Healthcare

on Economy and mostly on Public expenditure, the

efficient use of resources is a fundamental point

particularly during and after the recent global

economic crisis.

Process organization and resources allocation on

activities play a key role in achieving the required

service level and efficiency. For this purpose, the

knowledge of patient-flow is essential in order to

understand the current organization of processes and

to identify how resources could be allocated more

efficiently on organizational units (Vissers, 1998;

Haraden and Resar, 2004; Brailsford et al., 2004).

In this context, Diagnosis-Related Group (DRG),

as an empirical classification of the final products in

a hospital (Fetter and Freeman, 1986), represents a

topic of concern for research and a critical success

factor for healthcare systems worldwide. In fact, most

of the developed countries have already introduced

DRG-systems (Busse et al., 2011) for driving

resources allocation.

Despite DRGs classify patient groups with similar

expected patterns of resource use depending on their

diagnosis and treatments (Fetter and Freeman, 1986),

it is not still clear the association between DRGs and

related care processes (Mans et al., 2009). Therefore,

managers are not able to relate the resources received

(and theoretically consumed) with the process flow.

Difficulties in process discovery and analysis are

mainly due to the peculiar complexity of healthcare

processes: highly interconnected, patient dependant

and multi-disciplinary in nature (Anyanwu et. al,

2003; Mans et al., 2009; Rebuge and Ferreira, 2012).

Given these characteristics, the process mining

techniques are potentially useful in identifying

process workflow in healthcare environments

(Rebuge and Ferreira, 2012). This paper proposes a

methodology for creating DRG patient-flow using

process mining techniques. Through the proposed

approach, it may be possible to understand the various

sequences of activities associated with a DRG and,

thus, to identify and model the main unit of analysis

of a healthcare system. Expected contributions and

implications aim to support:

1. Process identification in order to better

comprehend the actual way of working (Weerdt

et al., 2013).

2. Process analysis in order to calculate

438

Stefanini, A., Aloini, D., Dulmin, R. and Mininno, V.

Linking Diagnostic-Related Groups (DRGs) to their Processes by Process Mining.

DOI: 10.5220/0005817804380443

In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 5: HEALTHINF, pages 438-443

ISBN: 978-989-758-170-0

performance indicators, assess resource

consumption, detect bottlenecks, (Mans, 2008;

Rebuge and Ferreira, 2012; Rovani et al. 2015)

and facilitate cost accounting, particularly in an

Activity Based Costing perspective.

3. Process improvement in order to streamline

business processes and optimize resource

planning. Moreover, the expected results may

also support regional or national authorities in

demand aggregation and resource planning at

district level.

Furthermore, the paper presents a case study based on

a real open data set of the AMC (Academic Medical

Center) hospital in Amsterdam, Netherlands.

2 THEORETICAL

BACKGROUND

The healthcare industry represents an important and

growing research context. It can be characterized on

providing individualized cares and, at the same time,

on efficiency (Dobrzykowski et al., 2013). In the last

years, scholars have strongly focused their attention

on many healthcare related sub-streams. In Operation

Management and Supply Chain Management

literature, the most prominent are: ICT and

Technology Assessment in healthcare (e.g. Chau and

Hu, 2001; Tzeng et al. 2008); Quality and Lean

Thinking (e.g. Stock et al., 2007; Aronsson 2011);

SCM and Strategy (e.g. Li et al. 2002; Lee et al.,

2011); Resource Planning, Capacity Management

and Scheduling (e.g. Bretthauer et al., 1998; Jebali et

al., 2006); Business Process Management and Patient

Flow in healthcare (e.g. Haraden and Resar, 2004;

Brailsford et al., 2004).

2.1 Patient-flow and Healthcare

Process

Patient-flow is the movement of patient, with related

information and equipment, between departments,

staff groups or organizations as part of a patient's care

pathway. It is characterized by a sequence of

processes/activities that a patient follows from the

first contact with the hospital to the discharge.

Usually activities in patient flow are highly

interconnected, heterogeneous and numerous, but all

of them are necessary for the achievement of final

outcome (Lenz and Reichert, 2007; Rebuge and

Ferreira, 2012; Partington et al., 2015).

The knowledge of patient-flow is very important

for healthcare organizations, because strongly

connected to effectiveness of care and efficiency of

resource allocation (Vissers, 1998; Haraden and

Resar, 2004; Brailsford et al., 2004). As a

consequence, some authors started studying patient-

flows for optimizing resource allocation both in the

short-term (e.g. scheduling of treatment) and

medium-long term (e.g. planning of resource inside

the hospital) (e.g. Vissers, 1998; Haraden and Resar,

2004).

Although business process analysis is recognized

as an extremely important activity for healthcare

organizations, traditional approaches are low

effective and scarcely efficient in such a context: they

are very time-consuming and may not provide an

accurate picture of the healthcare processes (dynamic,

multi-disciplinary, interconnected, numerous and ad

hoc) (Anyanwu et. al, 2003; Mans et al., 2009;

Rebuge and Ferreira, 2012; Rovani et al., 2015).

The recent introduction of process mining

techniques has partially changed the scenario helping

to overcome problems in finding and mapping

patient-flows. Until today, the studies on patient-flow

through process mining have mainly focused on

medical and methodological aspects (e.g. Bose and

Van der Aalst, 2011; Huang et al., 2012; De Weerdt

et al., 2013; Mans et al. 2013; Delias et al., 2015;

Rovani et al., 2015).

In particular, at our best knowledge, literature

does not present works that link the patient-flows

with the DRGs, i.e. the “final products” of hospitals.

The lack of a clear association between the healthcare

service and its process and, thus, cost structure, is an

important issue that needs to be faced in order to

improve the efficiency of healthcare systems.

2.2 Process Mining in Healthcare

Due to the complexity of healthcare processes,

process mining could be successfully applied to

support process (patient flow) discovery in healthcare

sector (Van der Aalst et al., 2007). Process mining, in

fact, aims to derive meaningful insights from the

complex temporal relationships existing between

activities and resources involved in processes

(Partington et al., 2015).

Although various authors have already proved its

appropriateness (e.g. Mans et al. 2008; Mans et al.,

2009; Huang et al., 2012; Rebuge and Ferreira, 2012;

De Weerdt, 2013; Rovani et al., 2015), the application

of process mining technique in healthcare is a

relatively new and unexplored field. In particular,

different authors have mined patient control-flow

using process discovery techniques.

In process discovery context, authors have

Linking Diagnostic-Related Groups (DRGs) to their Processes by Process Mining

439

oriented their attention on different issues. Some

scholars have proved and discussed the suitability of

process discovery technique to healthcare services

(e.g. Mans et al. 2008; Mans et al., 2009; Kaymak et

al., 2012; Mans et al., 2013), typically presenting

case studies. Other researchers have focused their

studies on proposing innovative methodologies

and/or new algorithms (e.g. Huang et al., 2012;

Rebuge and Ferreira, 2012; Delias et al., 2015).

Others have directed their attention on comparing the

discovered process workflow with the expected

pathways, the medical guidelines or the analogous

patient flows in other hospitals (e.g. Montani et al.,

2014; Caron et al., 2014; Rovani et al., 2015;

Partington et al., 2015).

On the other hand, the use of “conformance

checking” and “enhancement” process-mining

techniques seems to be still currently understudied in

the healthcare field (Partington et al., 2015).

Discovering the patient-flow is strongly

dependent on the adopted patient classification. Some

authors use new clustering techniques or clustering

tools to categorize patients (e.g. Mans et al., 2009;

Rebuge and Ferreira, 2012). Others categorize patient

basing on main treatment (e.g. De Weerdt, 2013;

Caron et al., 2014) or on a preliminary classification

inside the emergency centre (Partington et al., 2015).

Nobody has classified patient basing on DRG.

3 RESEARCH DESIGN

3.1 Research Objectives

This paper aims to suggest a methodology for

identifying and mapping patient-flow by process

mining, following the lens of DRGs.

Process mining is the combination between data

mining and traditional model-driven Business

Process Management (Van der Aalst, 2011). The

main purpose is to discover, analyze and improve

processes by extracting knowledge from event logs

readily available in enterprise information systems

(Van der Aalst, 2011; Van der Aalst et al., 2012).

Process mining in fact exploits information in the

event logs to understand how processes are actually

performed, rather then what is prescribed or supposed

to happen (Mans et al., 2009; Huang et al., 2012).

3.2 Research Methodology

The proposed methodology (Figure 1) refers to

process mining methods suggested by previous

authors (e.g. Bozkaya et al., 2009; Rebuge and

Ferreira, 2012; Mans et al., 2009; De Weerdt, 2013)

and consists of four phases: log preparation, log

inspection, process discovery, validation of process

model.

1. Log preparation aims to set up the event log by pre-

processing the event data gathered from one or more

information systems in order to select the adequate

unit of analysis for analysing the patient-flows.

2. Log inspection provides ﬁrst insights about the

investigated process. It includes statistical

information on the number of cases, the total number

of events, the number of different sequences, etc.. In

addition, these activities help to filter incomplete and

outlier cases.

3. Process discovery phase extracts the process model

from the event log. In order to point out a clear

process workflow from highly complex processes, it

could be also necessary to refine the final model

ruling out less frequent paths or excluding less

relevant activities. Therefore, the log may need to be

re-filtered looking at retaining events covering at least

80% of the cases (Bozkaya et al., 2009). Among

many available techniques to act process discovery

like α-Algorithm, heuristic miner, fuzzy miner,

genetic miner and region-based miner, most suitable

approaches for complex environment seems to be

heuristic mining, genetic mining and fuzzy mining

(Van der Aalst, 2011), which are specialized in noise

filtering. While highly performant in dealing with

noise, genetic process mining is not very efficient for

larger models and logs, due to excessive computation

times (Van der Aalst, 2011). Heuristic mining

algorithm enables users to focus on the main process

flow effectively, but it needs to be supported by the

application of clustering technique to the event log.

The fuzzy mining addresses the issue of mining

unstructured processes using a mixture of abstraction

and clustering techniques and attempts to make a

more suitable representation for analysts. Fuzzy

mining automatically provides a high-level view on

the process by abstracting and aggregating details

(Mans et al., 2009). Finally, some authors (e.g.

Figure 1: The proposed methodology.

HEALTHINF 2016 - 9th International Conference on Health Informatics

440

De Weerdt et al., 2013; Mans et al., 2009; Partington

et al., 2015) adopt heuristic and fuzzy mining

conjointly in order to exploit the advantage of both of

them.

In the case study, we have decided to use the fuzzy

mining for the motivations previously cited.

4. Validation is the final step. Results obtained, i.e.

patient-flow model related to the DRG, has to be

reviewed by experts in order to check for

completeness and correctness. More in-depth

validation analysis are also available, for example it

is possible to apply the conformance checking

technique on the achieved workflow model (Bozkaya

et al., 2009; Van der Aalst, 2011; Rovani et al., 2015).

Due to the complexity and the client dependency,

which are typical in healthcare processes, it is

expected that the patient-flow model presents more

than one possible path for the related DRG.

The method could be extended and applied to

different DRGs inside the hospital, to obtain the

overall process model of the organization.

3.3 Case Study

The case study tests the applicability of the proposed

methodology on an open dataset from a

gynaecological oncology process at the AMC

hospital in Amsterdam (doi:10.4121/uuid:d9769f3d-

0ab0-4fb8-803b-0d1120ffcf54). The repository

contains about 150.000 events of more than 1100

patient cases. Each case in the event log corresponds

to a single patient, so that data present a wide variety

of care activities (De Weerdt et al., 2013).

It should be noted that the case study is limited to

the first three stage of the methodology.

The first phase of log preparation was very

limited, because the datasets had already been

integrated and pre-processed by scholars at

“Technische Universiteit Eindhoven”. We just

selected the patient instances with the same group

diagnosis (“maligniteit ovarium|tuba”), recognizable

in The Netherlands as DBCs (equivalent to DRGs)

Figure 2: The discovered process model mined by Fluxicon

Disco® (the more infrequent activities are excluded).

although not yet introduced at that time. The number

of selected case was 60.

In this step, we also decided to designate the

hospital operational unit as the adequate level of

abstraction for our study and, thus, for aggregating

more specific activities. For example, blood test is

usually recorded like a set of different events with a

distinct name depending on the specific analysis. In

this case, we substituted this set with a single event

reporting the involved organization unit, the General

Lab Clinical Chemistry department.

The second phase of log inspection provided us

with a first impression about the processes through

event log data and some statistics such as the total

number of events, the number of different sequences

etc.. Outlier cases were removed. After this operation,

the dataset contained 59 cases.

Process discovery was conducted using the Fuzzy

Miner implemented by Fluxicon Disco®. We

analysed process behaviours and mapped the

workflow for the process under investigation. In

Figure 2, the less frequent activities have been

removed. Please note that just cutting out less than

10% of the events, the simplification in the process

map is very relevant. This is significant and let us

more confident about the suitability of the

methodology and the appropriateness of the selected

process mining technique.

Exploring the discovered process models allows

us to point out some interesting insight about the

patient-flow. For example, as shown in Figure 2, the

majority of patients start with a Gynaecological visit;

they deepen the diagnosis through a Laboratory

analysis or/and Radiological examination (sometimes

repeated more than ones); if serious diseases occur

the process goes on with a surgery operation and post-

operation care, otherwise patient usually end her path

after a final Gynaecological visit.

Figure 3: The discovered process model for a classify set of

cases (patients who need a surgical operation).

As an addition, the tool allows us to filter cases

using specific attributes and sketches patient-flow for

Linking Diagnostic-Related Groups (DRGs) to their Processes by Process Mining

441

a specific case or for a set of patients, e.g. to

understand specific practices and to find out possible

anomalies.

As shown in Figure 3, we were finally able to

determine the main process flow for patients who

need a surgical operation: the process generally starts

with a Gynaecological visit, then a Laboratory

analysis and a radiological assessment are executed,

hence the patient is hospitalized and other Laboratory

analysis can be done in the meanwhile, after that the

patient undergoes a surgical operation, an

examination at Pathology depart, again

Gynaecological visit and, if needed, additional

Laboratory analysis. Finally, the patient is discharged

from hospital.

Note that the process discovery phase, while

depicting the process flows, also reveals many

interesting information about how many times one

activity occurred, for example we found out that

Gynaecological visit takes place 144 times and

Laboratory analysis 304 times. This is useful in order

to define resource consumption and considerations

about process efficiency. This activity is useful to

support resource planning both in the short and

medium-long term. Looking at the DRG “maligniteit

ovarium|tuba”, we can state that a patient needs an

average of 2.5 Gynaecological visits, 5 Laboratory

analysis and 5.7 hospitalization days.

Besides, we can find some patterns of resource

consumption. For example, only in 38% of cases the

patient undergoes a surgical operation but these cases

implicate the 62% of diagnosis and treatments and the

77% of nursing activities. This evidence could be

useful in order to support process verification and

process analysis.

4 CONCLUSIONS

The case study supports the potential applicability of

the proposed methodology for process discovery in

the healthcare context. Process map and other

interesting insights about the patient flow for the

“maligniteit ovarium|tuba” cluster are obtained: most

relevant process path, number of cases, recurrence of

activities (for example Gynaecological visit or

Laboratory analysis), total hospitalization days and

resource consumption.

The comprehension of the patient flow (and

subsequent comparison of the actual way of working

within the hospital unit) enables to check and verify

business processes, to support process analysis and

improvement and to better define resource

consumption/allocation (Vissers, 1998). Besides, the

tool also provides simultaneous functionalities of

clustering, filtering and modifying the abstraction

level of the model, which allows decision makers to

understand processes for a specific set of patients.

The extension of this methodology to a hospital

system (i.e. mapping and integrating processes for all

the DRGs) might help managers to:

- Estimate the levels of activities and plan

hospital resources in the medium-long term.

- Support cost accounting, particularly in an

Activity Based Costing perspective.

- Take decisions accordingly to the different

“profitability levels” of various subsets of

patients.

Moreover, the approach can be also expanded to

wider care organizations, at district or regional level.

This would be helpful for regional or national

authorities, since it might support demand

aggregation and integrated resource planning, then

system efficiency.

This work also shows some limitations for the

proposed methodology. The main critical issue is the

possible concurrent coexistence of two or more

diseases (DRGs). These cases of comorbidity could

alter the final analysis and require more accurate/ad

hoc/time consuming pre-processing activities during

the log preparation or log inspection phases.

Furthermore, the case study concerns a limited

number of cases and a single DRG; this condition can

clearly affect the meaningfulness and generalizability

of the investigation.

However, results gained in this preliminary study

are mostly positive and encourage us to extend the

research to a wider set of DRGs to support hospital

process management. In future, we plan to assess a

new case study in collaboration with an Italian

hospital. This could allow gaining a more in-depth

test of the method and integrating the process

structure of n-DRGs in order to really support a

hospital resource planning system.

REFERENCES

Anyanwu, K., Sheth, A. P., Cardoso, J., Miller, J. A., &

Kochut, K. J. (2003). Healthcare enkmterprise process

development and integration.

Aronsson, H., Abrahamsson, M., & Spens, K. (2011).

Developing lean and agile health care supply chains.

Supply Chain Management: An International Journal,

16(3), 176-183.

Bose, R. J. C., & van der Aalst, W. M. (2011). Analysis of

Patient Treatment Procedures. In Business Process

Management Workshops (1) (pp. 165-166).

HEALTHINF 2016 - 9th International Conference on Health Informatics

442

Bozkaya, M., Gabriels, J., & Werf, J. M. E. M. (2009).

Process diagnostics: a method based on process mining.

In Information, Process, and Knowledge Management,

2009. eKNOW'09. International Conference on (pp. 22-

27). IEEE.

Brailsford, S. C., Lattimer, V. A., Tarnaras, P., & Turnbull,

J. C. (2004). Emergency and on-demand health care:

modelling a large complex system. Journal of the

Operational Research Society, 55(1), 34-42.

Bretthauer, K. M., & Cote, M. J. (1998). A model for

planning resource requirements in health care

organizations. Decision Sciences, 29(1), 243.

Busse, R., Geissler, A., & Quentin, W. (2011). Diagnosis-

Related Groups in Europe: Moving towards

transparency, efficiency and quality in hospitals.

McGraw-Hill Education (UK).

Caron, F., Vanthienen, J., Vanhaecht, K., Van Limbergen,

E., De Weerdt, J., & Baesens, B. (2014). Monitoring

care processes in the gynecologic oncology department.

Computers in biology and medicine, 44, 88-96.

Chau, P. Y., & Hu, P. J. H. (2001). Information technology

acceptance by individual professionals: A model

comparison approach. Decision Sciences, 32, 699-719.

De Weerdt, J., Caron, F., Vanthienen, J., & Baesens, B.

(2013). Getting a grasp on clinical pathway data: an

approach based on process mining. In Emerging Trends

in Knowledge Discovery and Data Mining (pp. 22-35).

Springer Berlin Heidelberg.

Delias, P., Doumpos, M., Grigoroudis, E., Manolitzas, P.,

& Matsatsinis, N. (2015). Supporting healthcare

management decisions via robust clustering of event

logs. Knowledge-Based Systems, 84, 203-213.

Dobrzykowski, D., Deilami, V. S., Hong, P., & Kim, S. C.

(2014). A structured analysis of operations and supply

chain management research in healthcare (1982–2011).

International Journal of Production Economics, 147,

514-530.

Fetter, R. B., & Freeman, J. L. (1986). Diagnosis related

groups: product line management within hospitals.

Academy of Management Review, 11(1), 41-54.

Haraden, C., & Resar, R. (2004). Patient flow in hospitals:

understanding and controlling it better. Frontiers of

health services management, 20(4), 3.

Huang, Z., Lu, X., & Duan, H. (2012). On mining clinical

pathway patterns from medical behaviors. Artificial

intelligence in medicine, 56(1), 35-50

Jebali, A., Alouane, A. B. H., & Ladet, P. (2006). Operating

rooms scheduling. International Journal of Production

Economics, 99(1), 52-62.

Kaymak, U., Mans, R., van de Steeg, T., & Dierks, M.

(2012, October). On process mining in health care. In

Systems, Man, and Cybernetics (SMC), 2012 IEEE

International Conference on (pp. 1859-1864). IEEE.

Lee, S. M., Lee, D., & Schniederjans, M. J. (2011). Supply

chain innovation and organizational performance in the

healthcare industry. International Journal of Operations

& Production Management, 31(11), 1193-1214.

Lenz, R., & Reichert, M. (2007). IT support for healthcare

processes–premises, challenges, perspectives. Data &

Knowledge Engineering, 61(1), 39-58.

Li, L. X., Benton, W. C., & Leong, G. K. (2002). The

impact of strategic operations management decisions

on community hospital performance. Journal of

Operations Management, 20(4), 389-408.

Mans, R., Schonenberg, H., Leonardi, G., Panzarasa, S.,

Cavallini, A., Quaglini, S., & van der Aalst, W. (2008).

Process mining techniques: an application to stroke

care. Studies in health technology and informatics, 136,

573.

Mans, R. S., Schonenberg, M. H., Song, M., van der Aalst,

W. M., & Bakker, P. J. (2009). Application of process

mining in healthcare–a case study in a dutch hospital

(pp. 425-438). Springer Berlin Heidelberg.

Mans, R. S., van der Aalst, W. M., Vanwersch, R. J., &

Moleman, A. J. (2013). Process mining in healthcare:

Data challenges when answering frequently posed

questions. In Process Support and Knowledge

Representation in Health Care (pp. 140-153). Springer

Berlin Heidelberg

Montani, S., Leonardi, G., Quaglini, S., Cavallini, A., &

Micieli, G. (2014). Improving structural medical

process comparison by exploiting domain knowledge

and mined information. Artificial intelligence in

medicine, 62(1), 33-45.

Partington, A., Wynn, M., Suriadi, S., Ouyang, C., &

Karnon, J. (2015). Process mining for clinical

processes: a comparative analysis of four Australian

hospitals. ACM Transactions on Management

Information Systems (TMIS), 5(4), 19

Rebuge, Á., & Ferreira, D. R. (2012). Business process

analysis in healthcare environments: A methodology

based on process mining. Information Systems, 37(2),

99-116.

Rovani, M., Maggi, F.M., De Leoni, M., Van Der Aalst, W.

(2015). Declarative process mining in healthcare.

Expert Systems with Applications, 42 (23), 9236-9251.

Stock, G. N., McFadden, K. L., & Gowen, C. R. (2007).

Organizational culture, critical success factors, and the

reduction of hospital errors. International Journal of

Production Economics, 106(2), 368-392.

Tzeng, S. F., Chen, W. H., & Pai, F. Y. (2008). Evaluating

the business value of RFID: Evidence from five case

studies. International Journal of Production

Economics, 112(2), 601-613.

Vissers, J. M. (1998). Patient flow-based allocation of

inpatient resources: a case study. European Journal of

Operational Research, 105(2), 356-370.

Van der Aalst, W. M., Reijers, H. A., Weijters, A. J., van

Dongen, B. F., De Medeiros, A. A., Song, M., &

Verbeek, H. M. W. (2007). Business process mining:

An industrial application. Information Systems, 32(5),

713-732.

Van Der Aalst, W. (2011). Process mining: discovery,

conformance and enhancement of business processes.

Springer Science & Business Media.

Van Der Aalst, W., et Al. (2012). Process mining

manifesto. In Business process management workshops

(pp. 169-194). Springer Berlin Heidelberg.

Linking Diagnostic-Related Groups (DRGs) to their Processes by Process Mining

443