INDEED–Utilization and Cross-Sectoral Patterns of Care for Patients Admitted to Emergency Departments in Germany: Rationale and Study Design [original]

METHODS

published: 16 April 2021

doi: 10.3389/fpubh.2021.616857

Frontiers in Public Health | www.frontiersin.org 1April 2021 | Volume 9 | Article 616857

Edited by:

Leos Navratil,

Czech Technical University in

Prague, Czechia

Reviewed by:

Falk Hoffmann,

University of Oldenburg, Germany

Ingo Meyer,

University Hospital of

Cologne, Germany

*Correspondence:

Antje Fischer-Rosinský

antje.fischer[email protected]

Specialty section:

This article was submitted to

Disaster and Emergency Medicine,

a section of the journal

Frontiers in Public Health

Received: 13 October 2020

Accepted: 18 March 2021

Published: 16 April 2021

Citation:

Fischer-Rosinský A, Slagman A,

King R, Reinhold T, Schenk L,

Greiner F, von Stillfried D,

Zimmermann G, Lüpkes C,

Günster C, Baier N, Henschke C,

Roll S, Keil T and Möckel M (2021)

INDEED–Utilization and

Cross-Sectoral Patterns of Care for

Patients Admitted to Emergency

Departments in Germany: Rationale

and Study Design.

Front. Public Health 9:616857.

doi: 10.3389/fpubh.2021.616857

INDEED–Utilization and

Cross-Sectoral Patterns of Care for

Patients Admitted to Emergency

Departments in Germany: Rationale

and Study Design

Antje Fischer-Rosinský1*, Anna Slagman1, Ryan King2, Thomas Reinhold2, Liane Schenk3,

Felix Greiner4, Dominik von Stillfried5, Grit Zimmermann6, Christian Lüpkes7,

Christian Günster8, Natalie Baier9,10, Cornelia Henschke 10,11, Stephanie Roll2,

Thomas Keil2,12,13 and Martin Möckel1

1Emergency and Acute Medicine (Charité Virchow Klinikum-CVK, Charite Campus Mitte-CCM), Charité–Universitätsmedizin

Berlin, Berlin, Germany, 2Institute of Social Medicine, Epidemiology and Health Economics, Charité–Universitätsmedizin

Berlin, Berlin, Germany, 3Institute of Medical Sociology and Rehabilitation Science, Charité–Universitätsmedizin Berlin, Berlin,

Germany, 4Department of Trauma Surgery, Otto von Guericke University Magdeburg, Magdeburg, Germany, 5Central

Research Institute for Ambulatory Health Care in Germany (Zi), Berlin, Germany, 6TMF–Technology, Methods, and

Infrastructure for Networked Medical Research, Berlin, Germany, 7OFFIS–Institute for Information Technology, Oldenburg,

Germany, 8Allgemeine Ortskrankenkasse (AOK) Research Institute–Wissenschaftliches Institut der AOK (WIdO), Berlin,

Germany, 9Kiel Institute for World Economy, Kiel, Germany, 10 Department of Health Care Management, Berlin University of

Technology, Berlin, Germany, 11 Faculty of Health Sciences Brandenburg, Brandenburg University of Technology

Cottbus-Senftenberg, Cottbus, Germany, 12 Institute for Clinical Epidemiology and Biometry, University of Würzburg,

Würzburg, Germany, 13 State Institute of Health, Bavarian Health and Food Safety Authority, Bad Kissingen, Germany

Introduction: The crowding of emergency departments (ED) has been a growing

problem for years, putting the care of critically ill patients increasingly at risk. The

INDEED project’s overall aim is to get a better understanding of ED utilization and to

evaluate corresponding primary health care use patterns before and after an ED visit

while driving forward processes and methods of cross-sectoral data merging. We aim to

identify adequate utilization of EDs and potentially avoidable patient contacts as well as

subgroups and clusters of patients with similar care profiles.

Methods: INDEED is a joint endeavor bringing together research institutions and

hospitals with EDs in Germany. It is headed by the Charité–Universitätsmedizin Berlin,

collaborating with Otto von Guericke University Magdeburg, Technische Universität

Berlin, the Central Research Institute of Ambulatory/Outpatient Health Care in Germany

(Zi), and the AOK Research Institute as part of the Federal Association of AOK, as well

as experts in the technological, legal, and regulatory aspects of medical research (TMF).

The Institute for Information Technology (OFFIS) was involved as the trusted third party

of the project. INDEED is a retrospective study of approximately 400,000 adult patients

with statutory health insurance who visited the ED of one of 16 participating hospitals

in 2016. The routine hospital data contain information about treatment in the ED and, if

applicable, about the subsequent hospital stay. After merging the patients’ hospital data

from 2016 with their outpatient billing data from 2 years before to 1 year after the ED

Fischer-Rosinský et al. Cross-Sectoral Utilization of Emergency Patients

visit (years 2014–2017), a harmonized dataset will be generated for data analyses. Due

to the complex data protection challenges involved, first results will be available in 2021.

Discussion: INDEED will provide knowledge on extracting and harmonizing large scale

data from varying routine ED and hospital information systems in Germany. Merging these

data with the corresponding outpatient care data of patients offers the opportunity to

characterize the patient’s treatment in outpatient care before and after ED use. With this

knowledge, appropriate interventions may be developed to ensure adequate patient care

and to avoid adverse events such as ED crowding.

Keywords: emergency department, routine health care data, cross-sectoral data analysis, inadequate utilization,

ambulatory care sensitive conditions

INTRODUCTION

Emergency departments (EDs) in many countries face the

challenge of crowding and increasing numbers of ED visits (1).

The number of ED visits has increased over the last decades

in almost all OECD countries (2). Annually around 21 million

patients are treated in German EDs (3). From 2009 to 2015 the

number of patients in EDs increased by 42% for outpatient care

while inpatient emergency cases grew by 20% (4). One reason

for the rising ED patient numbers in outpatient treatment is

the utilization of the EDs’ 24/7 available medical expertise for

minor health problems. This is due to several reasons, with

patients often citing health anxiety and lack of alternatives (5–

7). More than 50% of ED patients do not require subsequent

hospital inpatient treatment, but approximately only 20% could

have been treated in the outpatient sector/outpatient care (8,9).

Care provision for patients with low urgency health needs in the

emergency setting is currently being heavily debated as hospital-

based outpatient care is associated with high costs and does

not offer the same continuity as primary care in the outpatient

sector (10). Additionally, a large share of acute care patients

receiving inpatient treatment after an ED visit are diagnosed with

Ambulatory Care Sensitive Conditions (ACSC), i.e., frequent

chronic and acute diagnoses for which inpatient care could have

Abbreviations: AV-data, medical prescription data; CDM, Central Data

Management; Case-Nr, the internal hospital Case Numbers for each ED patient

visit; DSSG, Service & Support GmbH (KV data trusted third party); eGK-

Nr′electronic health insurance card number; ED, emergency department; IDAT,

person Identifying Data; I-PNr, INDEED patient number (created in second stage

pseudonymization, i.e., pseudonymization of the pseudonymized eGK-Nr and

Name-DOB); INDEED, Utilization and cross-sectoral patterns of care for patients

admitted to emergency departments in Germany; KV, Association(s) of Statutory

Health Insurance Physicians (responsible for regions that correspond mostly to

the federal states in Germany); KV-data, the outpatient care data originating from

the KV; L-ID, line identifier used to link the IDAT and MDAT in CDM; MDAT,

Medical information user Data; MiG, Department of Health Care Management

of the Berlin University of Technology; Name-DOB, a combination of family

name, first name and date of birth; OFFIS, Institute for Information Technology,

Oldenburg–trusted third party of the project data; TMF, Technology, Methods, and

Infrastructure for Networked Medical Research; WIdO, AOK Research Institute,

Federal Association of AOK, Berlin, Germany; UKMD, Department of Trauma

Surgery, Otto von Guericke University Magdeburg, Magdeburg, Germany; Zi,

Central Research Institute of Ambulatory/Outpatient Health Care in Germany,

Berlin, Germany.

been avoided by timely and adequate measures in the outpatient

care sector (11–14).

Therefore, the INDEED-project (Utilization and cross-

sectoral patterns of care for patients admitted to emergency

departments in Germany) will (1) explore the utilization

and cross-sectoral patterns of care for patients admitted to

EDs in Germany, and (2) provide a framework for future

data linkage that takes into account the ethical, legal, and

technological aspects for creating a unique data set by

merging data from different sectors over a time period

of 3 years.

METHODS

Design

INDEED is designed as a retrospective evaluation of three

different scenarios based on different data sources (Figure 1),

with the aim of illustrating ED use in 2016. The main research

question focuses on scenario 1, comprising selected routinely

collected data on emergency treatment and possible subsequent

hospital stay from 16 EDs (data source 1: hospital data),

merged with the data of these patients from their routine

outpatient health care for the period from 2 years before to

1 year after their ED stay (data source 2: outpatient care

data) (15,16).

Scenario 2 utilizes the data for all patients in Germany

with statutory health insurance and an outpatient ED visit in

2016 from the collection of nationwide outpatient billing data

of the Associations of Statutory Health Insurance Physicians

(KV: Kassenärztliche Vereinigungen). Since outpatient treatment

of all statutory health insurance companies are invoiced via

the KV, including outpatient ED treatment (17), scenario 2

will allow the nation-wide representativeness of scenario 1 to

be assessed.

Similarly to scenario 2, as another independent and parallel

analysis, scenario 3 will analyze the outpatient and inpatient

care before and after an emergency treatment in 2016 using

routine data from a system of eleven regional health care funds of

AOK (Allgemeine Ortskrankenkassen, statutory health insurance

companies), that insure more than 26 million people in Germany

(data source 3: AOK routine data).

The present publication focuses on scenario 1.

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Fischer-Rosinský et al. Cross-Sectoral Utilization of Emergency Patients

FIGURE 1 | Project scenarios 1–3 using the evaluation of different data sources.

In order to achieve the goals of INDEED scenario 1, inclusion

of all eligible patients is crucial to guarantee representativeness

of the study population. Treatment in an ED is generally carried

out in an exceptional situation; in some cases patients are

not able to give consent due to urgency of treatment and the

short treatment period. Therefore, individual declarations of

consent are/were not intended in the context of this retrospective

secondary use of routine data for INDEED. Instead, data

processing had to be based on a statutory norm of consent,

requiring complex technical and organizational measures as

described below.

Study Population

EDs were recruited by approaching clinics from an existing

ED-network, AKTIN (18), as well as at scientific events of

the Emergency Medicine societies in Germany. We intended

to recruit hospitals of different sizes throughout Germany.

The main requirement for participation was the availability of

electronic documentation in the ED in 2016. Data protection

regulations in the respective federal states were also taken

into consideration and three categories of feasibility were

assigned to the federal states of Germany (green, yellow,

red). It was examined whether there was a legal norm of

permission at the state level to conduct the project without

prior informed consent of patients (green−7 times assigned) and

which requirements, if any, were associated with this (yellow−3

times). In the absence of such or significant restrictions, the

project was assigned to the red category (6 times). Regardless

of the categorization, it should be noted that in each case a

considerable great effort of argumentation was done to obtain the

necessary approvals.

Patients in the participating EDs were included if they

had at least one ED visit in 2016 and were insured in one

of the existing German statutory health insurance companies,

which cover about 87% of the population in Germany (19).

We exclude patients with private health insurance as well as

cases that are billed via the German Social Accident Insurance.

The latter covers accidents at work or recognized occupational

diseases. Although they constitute a relevant proportion of cases

in German EDs, they are covered by a different regulatory

framework. Minimum age has to be 20 years on January 1st 2016,

as this corresponds to a minimum age of 18 years at the beginning

of our observation period (January 1st, 2014) in the outpatient

care data.

The final study population for scenario 1 are ED patients

originating from 16 participating EDs in Germany (Figure 2).

In addition, basic data of ED and hospital structures

are collected according to standards used in previous

projects such as the German DGINA (German society for

interdisciplinary emergency and acute medicine) network

(20), to allow a basic description of the participating EDs

and hospitals.

Data Sources, Data Flow, and Management

In scenario 1, data from two different sources will be merged.

Firstly, ED and inpatient data from 2016 will be extracted from

16 hospitals with EDs across Germany. This will include general

information about the patient, ED treatment data, vital signs,

blood parameters, and data from a subsequent inpatient stay.

Secondly, for the time period from 2014 to 2017 outpatient

care data will be extracted for all patients who were treated

at least once in 2016 in one of the participating EDs. This

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Fischer-Rosinský et al. Cross-Sectoral Utilization of Emergency Patients

FIGURE 2 | Overview of the 16 participating hospitals with emergency departments (red dots) and the German federal states (in gray) where the regional Associations

of Statutory Health Insurance Physicians provided outpatient health care data. “Charité Berlin (1–3)” designated 1 =Campus Virchow Klinikum, 2 =Campus Mitte,

3=Campus Benjamin Franklin.

data will be provided by the Zi after collection from the

regional Associations of Statutory Health Insurance Physicians

(KV) of the federal states corresponding to the location of

the participating EDs. These data sets will include general

information about the patient, information on the medical

practice and practitioner, diagnoses, performed procedures

and their costs, and information about the medication and

their costs.

Clinical data usually lack sufficient standardization due to

different software systems used for documentation in the EDs,

different treatment pathways, different routines and situations,

with an additional lack of time for sufficient documentation in

emergency situations. The degree of heterogeneity and need for

further homogenization and data-coarsening will be determined

after data extraction from all hospitals has been completed.

Data processing will take place at the INDEED Central Data

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Fischer-Rosinský et al. Cross-Sectoral Utilization of Emergency Patients

Management (CDM) office. The list of variables to be extracted

from the hospitals and the respective outpatient care data are

presented in Supplementary Table 1.

The data handling procedures from the local hospital queries,

the linkage of both data sources (hospital and outpatient care

data), up to the final dataset are quite complex: challenges include

(i) extracting data from different documentation systems, (ii)

cleaning and harmonizing them, and (iii) linking these two data

sources (hospital data and outpatient care data) at the patient

level, all while conforming to the different and complex data

protection rules (Figure 3). All processes regarding data linkage

follow and adhere to the “Good Practice Data Linkage” (21).

The data flow (step 1 to 9) is described in more detail in

the following section. All data containers are signed and RSA-

4096-encrypted (special encryption method named after Rivest,

Shamir and Adleman) and all data transfers are handled via

SSH/TSL, secured by encryption on transfer level and receiver

oriented encryption on file level (22).

Extraction and Pseudonymization of Hospital Data

For the linkage of the two data sources (hospital and outpatient

care data), an identification number is needed that is unique

for each patient and available in both data sources (Figure 3,

Step 1). This linking will be based primarily on the electronic

health insurance card number (eGK-Nr) and, secondarily, on a

combination of surname, first name, and date of birth (Name-

DOB; DOB: date of birth) if the eGK-Nr is missing or erroneous.

However, in the end it only worked for the first strategy based

on the eGK-Nr, due to software issues and lack of time to

fix this problem. Of the final 353.926 ED-patients, 290.883

(82.2%) had a valid eGK number, could be identified in the KV

data and successfully linked. The eGK-Nr, Name-DOB and the

internal hospital case numbers (Case-Nr) for each ED patient

visit compose the patients’ Identifying Data (IDAT). A computer

software, which was specifically developed for the INDEED-

project by OFFIS, pseudonymizes these IDAT after final data

extraction using a cryptographic hash function. The data set

containing the IDAT, the Medical information user Data (MDAT)

and an added line identifier (L-ID) is split into separate IDAT

and MDAT datasets, with the L-ID in both, and consequently

into different encrypted containers. The MDAT include all the

information regarding treatment during the ED visit and the

possible subsequent inpatient stay. The MDAT can only be

decrypted in CDM. Since hospital data will be obtained from

different documentation systems within each hospital the above

process must be performed on numerous data sets for each

hospital. Although these numerous data sets could potentially be

merged on-site with the Case-Nr it will be performed in CDM at

a later stage, to ensure the highest data quality.

Transfer of Hospital Data

The hospital data will be securely transferred and stored on a

server at OFFIS (Figure 3, Step 2). A random center-specific

number will be assigned to each hospital, and this allocation is

only known by OFFIS. This number will allow center-specific

adjustments in the statistical analyses. However, the consortium

agreement prohibits comparative center-specific analyses.

Preparation of Data Linkage at OFFIS: Hospital Data

OFFIS will create a list of INDEED patients based on

the pseudonymized eGK-Nr and Name-DOB in the IDAT,

additionally using the Case-Nr if inconsistencies occur (Figure 3,

Step 3).

Selection of Outpatient Care Data and Data Transfer

to the Zi

The list of pseudonyms will be imported into the INDEED-

software and applied to the outpatient care data from 2014 to

2017 of the KV in the relevant regions (Figure 3, Step 4). Patients

who are included in INDEED (i.e., having at least one visit in

one of the cooperating EDs in 2016) will be extracted from

this data. Then, a separation of the MDAT and pseudonymized

IDAT and a subsequent encryption will be applied as per the

hospital data.

Generation of the Medical Prescription Data

Pseudonym at the Trusted Third Party of the KV and

Medical Prescription Data Selection at the Zi

For the medical prescription data, the INDEED patients will be

pseudonymized in a separate process by the KV trusted third

party (Figure 3, Step 5). The Zi who already possesses the medical

prescription data and the corresponding pseudonyms created by

the trusted third party will then extract the INDEED patient’s

medical prescription data and merge it with the KV data.

Data Transfer of KV and Medical Prescription Data to

OFFIS

In the next step, the Zi transfers KV and medical prescription

data (from steps 4 and 5) of the INDEED-patients to OFFIS

(Figure 3, Step 6). The IDAT can only be decrypted by OFFIS,

the MDAT can only be decrypted in CDM.

Data Linkage (OFFIS) and Data Transfer to Central

Data Management

The patient pseudonyms within the IDAT (eGK-Nr and Name-

DOB) for both the KV and medical prescription data as well as

the hospital data are then replaced with a new single pseudonym

at OFFIS, to produce the INDEED patient number (I-PNr)

(Figure 3, Step 7). Together with the MDAT, these IDAT are then

transferred to CDM.

Data Processing (Data Management, Cleaning,

Harmonization, Plausibility, Etc.)

Each variable for each hospital will be cleaned (e.g., irrelevant

text removed, data formats standardized) and homogenized

(e.g., standardized units of measurement and missing value

symbols) (Figure 3, Step 8). Variables with varying response

categories between hospitals will be harmonized using

standardized categories determined by experienced clinicians

and methodologists of the consortium, who will also determine

plausibility rules (e.g., min/max cut-offs, logical data values) and

the rules for implausible values (including exclusion).

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Fischer-Rosinský et al. Cross-Sectoral Utilization of Emergency Patients

FIGURE 3 | Schematic overview of the data flow in INDEED scenario 1. ED, emergency department; OFFIS, Institute for Information Technology; Oldenburg, trusted

third party of the project data; CDM, Central Data Management; KV-data, the outpatient care data originating from the KV; DSSG, Service & Support GmbH (KV data

trusted third party); AV-data, medical prescription data; C-Not, Emergency and Acute Medicine (CVK, CCM); Charité, Universitätsmedizin Berlin; C-Soz, Institute of

Medical Sociology and Rehabilitation Science; Charité, Universitätsmedizin Berlin; C-Epi-DA, Institute for Social Medicine, Epidemiology and Health Economics;

Charité, Universitätsmedizin Berlin, Data analizes; UK Magdeburg, Department of Trauma Surgery; Otto von Guericke University Magdeburg, MiG Berlin, Dept. Health

Care Management, Berlin University of Technology; Zi, Central Research Institute of Ambulatory/Outpatient Health Care in Germany.

Database Structure and Data Provision to the

Analyzing Institutions

Due to its structure and size, data will be stored in a relational

database and CDM will provide partitions of the processed data

to the analyzing partner institutions within the consortium upon

request (Figure 3, Step 9). In accordance with the consortium’s

agreement, the partner institution will initiate this process by

informing the management board (MB) about the proposed

research question(s) to be analyzed and the respective data

needed. The MB will decide if the research question is appropriate

under the general INDEED scopes, taking other partner’s

research areas into account. Afterwards, the analyzing partner

officially requests the data from the MB’s Data-Use-And-Access-

Committee (DUAC). The DUAC checks whether the requested

variables are suitable for answering the research questions. Upon

approval, the DUAC will inform CDM, who will then prepare the

relevant data set. Before release of the data, CDM will generate

new random ID-numbers for each specific analysis, replacing

the identifying variables originating from the IDAT (e.g., I-PNr,

Case-Nr) to ensure that the partners will be provided only with

factually anonymized data. In addition, data-coarsening may

take place before data release for variables that may potentially

be used to identify an individual person (e.g., by categorizing

variables into broader classes, or by eliminating or categorizing

extreme values).

Sample Size Estimation and Statistical

Analyses

We assumed that 15 to 20 EDs across Germany will result in a

sample of EDs yielding different hospital structures and locations.

We were assuming that these EDs have an average of about 34,000

cases per year. This assumption is based on a general estimate of

the average number of cases in German EDs (3,23,24), and yields

an expected total number of 510,000 to 680,000 cases. However,

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Fischer-Rosinský et al. Cross-Sectoral Utilization of Emergency Patients

since about 15% of patients have multiple emergency contacts per

year (based on ED data from Charité in Berlin), the estimated

total number of patients is around 442,000 to 589,000. Applying

the study’s inclusion criteria (e.g., adult patients with statutory

health insurance) will limit the number of eligible patients to

about 86.5% of the total population in Germany in 2016 (24).

Thus, we expect a total of approximately 400,000 ED patients to

be available for our data analysis.

With this sample size, precise estimates of prevalences

are possible even within small subgroups: for example,

in any given subgroup of 1% of the total number of

patients (i.e., approximately 4,000 patients) a prevalence

of 50% could be estimated with a precision of ±1.6%

(half width of a two-sided exact 95% confidence interval).

In this example, a prevalence of 50% was chosen, as this

proportion shows the greatest variation. A prevalence

higher or lower than 50%, or subgroups larger than 1%,

would lead to narrower confidence intervals, i.e., more

accurate estimates.

Data Protection and Ethical Aspects

Linking health care data on an individual level requires a high

effort of data protection and data security as well as compliance

with ethical standards. For the project the ethical principles of

the Helsinki Declaration will be taken into account (25,26) and

the legally compliant handling of medical treatment data and

personal data will be ensured.

The study was approved by the ethics committee of the

Charité–Universitätsmedizin Berlin (application: EA4/086/17).

The data protection concept applies to all linked datasets and

considers the corresponding flow, time and scope of data

linkage and data analyses, was approved by the TMF Working

Group of Data Protection on the 14th February 2018 as

well as by the institutional data protection officer at Charité–

Universitätsmedizin Berlin.

The legal context of collecting routine treatment data for

each hospital depends upon whether it is a public or private

entity (27). In addition, both federal and state data regulations

have to be considered. The legal framework in Germany

for social data has been set out in the Social Code Books,

especially SGB X, V und I. The social data of the KV are

especially protected (§ 35 SGB I) and data transfer must

be approved according to the legal regulations of paragraphs

§§67a ff. SGB X. The basis for transferring social data for

the present research project is paragraph § 75, Section 1,

number 1, SGB X (research projects on the basis of social data)

and requires approval by the responsible regulatory authority

(VfD_INDEED_17_003844; NCT03224078).

The INDEED project received all necessary approvals from

the responsible regulatory authorities, before the respective

hospital data extraction was started. The last approval was

granted by June 2019. For data protection, this included 16

approvals from the data protection officers of the participating

hospitals, two approvals from the data protection authorities of

the federal states as well as eight approvals from the responsible

regulatory authorities for social data.

EXPECTED RESULTS AND STATUS QUO

The central research question is to identify and characterize

patients with an adequate, inadequate, or avoidable ED

utilization. Further research questions include: Which outpatient

care did patients receive before and after their stay in an ED

and what are influencing factors of ED utilization? An additional

focus will be put on the analysis of vulnerable subgroups (e.g.,

multimorbid patients, elderly patients). The results have the

potential to contribute to the development of health policy

innovations and interventions for need-based, purposeful, and

economic adjustment of care processes and structures. Identified

patient clusters shall be used to adjust health care across

sectoral boarders.

The methodological aim of INDEED is to create an

infrastructure that allows the linkage and use of routinely

collected ED and hospital data in combination with routine

outpatient health care data. This includes the development

and implementation of a sustainable/generic data protection

concept, the standardization of emergency care data across

varying hospital information systems and the identification of

routinely collected key characteristics of the outpatient sector.

A main challenge will be the linkage of individual patient data

from different care providers based on pseudonyms. Compared

to other studies on EDs, we are able to include patients

across outpatient and inpatient care from different German

statutory health insurance funds. Previous studies focused either

on analyzing data from a single disease (28) or investigated

German wide emergency data separately for the inpatient and

outpatient sector (4).

All 16 EDs have completed the data extraction process by

September 2019 and their data has been transferred in double

pseudonymized form to the CDM. The data included general

information about the patient, ED treatment data and data of

the following in-patient stay. The availability of the data, its

characteristics and its quality was heterogeneous and varied

between EDs. A high level of data processing is required, which

is currently at an advanced stage. The last data set from the KV

was transferred to CDM at the beginning of November 2020. The

linking of this ED-data with the outpatient care data is work in

progress and a merged data set for analysis is expected by the end

of 2020.

International studies have shown that the linkage of existing

routine data enables cross-sectoral and interdisciplinary health

services research and can thus form the basis for interventions in

health care (29,30). In Germany, research usually focuses on the

analysis of health care situations within individual sectors or on

patients with specific diagnoses. Hence, the results are limited in

their informative value for emergencies in which the focus is not

on diagnosis but on symptoms (8). The linkage of ED data and

routine outpatient health care data at the individual level has so

far not been conducted in Germany.

However, routine data has been linked both nationally and

internationally for the analysis of cross-sectoral care in settings

differing from our project, helping to provide a less distorted view

of the reality of medical care provision. A few cross-sectional and

longitudinal analyses have been conducted, but not related with

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emergency care treatment (31,32). Such analyses counterbalance

some of the disadvantages of surveys, such as the recruitment of

hard-to-reach patient groups, selection, and recall bias.

The hospitals participating in INDEED are institutions

with long established structures and the responsible staff have

previously been active in emergency medicine research. Some

hospitals interested in INDEED were not able to participate

due to limited resources or personnel, insufficient IT support

capacity, lack of electronic documentation or access to it, or

a change of hospital contact person. An especially challenging

obstacle was the specific responsibility of each federal state in

Germany regarding data protection regulation. For example, the

Bavarian state hospital law does not allow non-anonymized data

to leave the hospital. Since the aim of the INDEED project is to

link hospital data with outpatient health care data, we were not

able to include any interested hospitals from Bavaria.

Although we included 16 hospitals from across Germany,

we did not randomly choose them and do not consider this

selection as being representative for Germany. Our findings can

therefore only be interpreted in a local context, i.e., the catchment

area of the hospital. However, since we will include data from

various types and a considerable number of university and non-

university hospitals in different federal states, we expect to obtain

a good picture of the cross-sectoral pathways of ED patients in

the German health care system.

A further limitation of INDEED is the linkage of hospital data

with outpatient treatment data held by the regional Associations

of Statutory Health Insurance Physicians, since there are also

privately billed health services that do not appear in these

data. Furthermore, treatments reimbursed by the German Social

Accident Insurance, i.e., in connection with an occupational

accident, are not included either. Future research projects should

also address these subgroups.

One strength of INDEED are the three scenarios

using different large data evaluation approaches. The

representativeness of the included patients and the results

from the INDEED scenario 1 cohort will be checked by

comparing them with specific characteristics of the other large

cohorts from scenarios 2 and 3, whose data originates from

the Associations of Statutory Health Insurance Physicians and

the AOK.

The extraction of pseudonymous data without consent from

the hospitals requires a very comprehensive data protection

concept. This was prepared under the leadership and with the

specific technological and methodological expertise of the TMF

as a consortium partner. The concept was thoroughly checked

and discussed by the multidisciplinary working group of the

TMF for data protection and subsequently approved by this

group. It was then made available to all the local data protection

authorities of the hospitals. In Thuringia and Brandenburg it

was necessary to obtain additional and explicit approval from

the federal state data protection authorities, a time-consuming

process, which had not been expected upon application and

after the first experiences with other federal states. The data

protection concept for the outpatient treatment data from

the Associations of Statutory Health Insurance Physicians was

institute for this specific outpatient health care data. They

transferred the application and the concept to the respective eight

regional Associations of Statutory Health Insurance Physicians,

which had to obtain the relevant approvals from the federal

state authorities as well. This resulted in further long and

bureaucratic processes lasting between several months to over

a year.

DATA AVAILABILITY STATEMENT

Due to the very high sensitivity of the data in the project, it

is not possible to make them available to the public. In the

central data management of the project the data are already

pseudonymised twice. They are made available exclusively to the

analyzing partners of the consortium in anonymised form and

only with variables that are matched to the research question.

ETHICS STATEMENT

The studies involving human participants were reviewed and

approved by Ethikkommission der Charité-Universitätsmedizin

Berlin/Germany. Written informed consent for participation was

not required for this study in accordance with the national

legislation and the institutional requirements.

AUTHOR CONTRIBUTIONS

CL was involved in creation of a new software used for

the project. All authors made substantial contributions to the

conception of the project and have drafted the publication.

FUNDING

INDEED was funded by the Innovation Fund of the Federal

Joint Committee (G-BA: Gemeinsamer Bundesausschuss,

01VSF16044). G-BA is the highest decision-making body of

the joint self-government of physicians, dentists, hospitals, and

public health insurance companies in Germany. The funding

agency has a controlling function with regard to the timing and

the content of the project described in the application.

ACKNOWLEDGMENTS

We are grateful to all participating study partners and their

staff. Representing the cooperating emergency departments

we would like to thank the respective head physicians:

M. Bernhard (University Hospital Düsseldorf), H. J. Busch

(University Hospital Freiburg), C. Wrede (Helios Clinical Center

Berlin Buch), R. Somasundaram (Charité–Universitaetsmedizin

Berlin, Campus Benjamin Franklin), T. Schoepke (Barnim

Hospital), E. Weidmann E (Ruppiner Hospital in Neuruppin),

B. Flasch [Frankfurt (Oder) Hospital], H. Hoeger-Schmidt

(Chemnitz Hospital), A. Gries (University Hospital Leipzig),

C. Schwarz (Sana Hospital, Leipzig County), W. Behringer

(University Hospital Jena), B. Erdmann (Wolfsburg Hospital),

S. Blaschke (University Hospital Goettingen), S. Wolfrum

Frontiers in Public Health | www.frontiersin.org 8April 2021 | Volume 9 | Article 616857

Fischer-Rosinský et al. Cross-Sectoral Utilization of Emergency Patients

(University Hospital Luebeck). Also many thanks to the former

and active members of the INDEED-study group for all their

contributions, in particular: S. L. Kuhlmann, T. Keller, M.

Liedtke, B. Riens, F. Staeps, K. Budzyak, A. Schneider, F. Walcher,

D. Brammen, W. Schirrmeister, M. Erhart, S. Carnarius, T.

Czihal, S. Eichler, M. L. Rosenbusch, D. Schreiber, C. Krause,

J. Drepper, N. Bethge, T. Schneider, S. Straub, B. Kreye, R.

Roehrig, P. Droege, T. Ruhnke, A. Kloess. We thank S. Binting for

creating the map with the participating emergency departments

and federal states providing outpatient health care data [using

EASYMAP 11.0 SP 6 (@2018 Luttum +Tappert DV-Beratung

GmbH, Bonn)].

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found

online at: https://www.frontiersin.org/articles/10.3389/fpubh.

2021.616857/full#supplementary-material

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Conflict of Interest: The authors declare that the research was conducted in the

absence of any commercial or financial relationships that could be construed as a

potential conflict of interest.

Stillfried, Zimmermann, Lüpkes, Günster, Baier, Henschke, Roll, Keil and Möckel.

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