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Master's Programme in Health Informatics
Spring Semester 2015
Degree thesis, 30 Credits
Vital signs in Swedish emergency departments
Qualitative analysis of factors influencing data quality
Author: Joana Vicente
Author: Joana Vicente
Main supervisor: Dr . Niclas Skyttberg, Department of LIME, Karolinska Institutet
and Capio St. Göran Hospital
Co supervisor: Prof. Dr. Sabine Koch, Department of LIME, Karolinska Institutet
Co supervisor: Dr. Rong Chen, Department of LIME and Cambio Healthcare Systems
Examiner: Dr. Maria Hägglund , Department of LIME, Karolinska Institutet
Master's Programme in Health Informatics
Spring Semester 2015
Degree thesis, 30 Credits
Affirmation
I hereby affirm that this Master thesis was composed by myself, that the work
contained herein is my own except where explicitly stated otherwise in the text.
This work has not been submitted for any other degree or professional
qualification except as specified; nor has it been published.
Stockholm, May 2015
Joana Vicente
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Master's Programme in Health Informatics
Spring Semester 2015
Degree thesis, 30 Credits
Vital signs in Swedish emergency departments
Qualitative analysis of factors influencing data quality
Abstract
Background: Triage is a commonly used sorting method for emergency
department patients and includes several assessments such as vital signs
measurement. Patient related information collected during triage, needs to be
documented in the patient´s electronic health record in order to be used during and
after the emergency care visit. The potential to use and re-use that information
depends on its quality.
Aim: The aim of this thesis was to explore the factors that affect data quality
during triage in Emergency Departments.
Objectives: The first objective was to describe the patient flow and
documentation processes in Swedish EDs. The second objective was to collect
staff´s perspectives in the quality of information that is documented in these
departments.
Methods: A qualitative study was performed through interviews with fourteen
care professionals analyzed by thematic content analysis. ED interview results
were analyzed using a framework for definition of data quality dimensions.
Results: Two main factors influencing vital sign´s data quality at the ED were
revealed: human and technical factors. All coded factors that influenced data
quality at the ED could be related to completeness, correctness and currency
dimensions for data quality. Analysis of patient flow and the documentation
processes at the ED showed that clinical measurement of vital signs leads to a
simultaneous need for documentation routines. The care providers identified
several potential data quality influencing factors and pointed towards a symbiosis
between their documentation needs and the available technical solutions as a
possible way to improve vital signs data quality at the ED.
Conclusion: The main contribution of this thesis is the identification of human
and technical factors that impact completeness, correctness and currency of vital
sign data in the emergency department. For electronic systems to work reliable on
this data the introduction of standardized documentation is essential.
Keywords: Data quality; Triage; Vital signs; Emergency department.
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Acknowledgements
To my Supervisors
Thank you to all my supervisors, for supporting me through this work by
providing encouragement, knowledge, experience and support.
Dr. Niclas Skyttberg (my main supervisor) thank you for making this study
possible by providing clinical insights, engagement, feedback and support during
all times.
Prof. Dr. Sabine Koch, thank you for the academic guidance and Health
Informatics knowledge as well as the patience and support during this work.
Dr. Rong Chen, very special thanks for the technical Health Informatics insights,
experience, feedback and knowledge.
To all involved in this work
I want to thank all the study participants for giving me the chance to create this
work and taking the time to participate in the interviews.
To Karolinska Institutet
Thank you to all the 2015 Health Informatics teachers at Karolinska Institutet, for
two amazing academic years and for showing me that ”O saber não ocupa lugar”
[Knowledge doesn´t take place].
A special thanks to the 2015 Health Informatics class students for making me rich
in friendships, experiences and languages! A special thanks for both Blanca and
Julie for the muffin therapy sessions.
To my family
I want to thank my parents, with all my love, for always believing in me and
giving me the strength to move forward. Obrigado Mumy e Papi.
A loving thanks also to Tobias for, unconditionally, supporting me through these
two years. Thank you.
In the memory of my grandparents, Maria Vicência and Joaquim Quitério,
because “Relembrar é viver” [To remember is to live].
Finally, I would also like to thank Be (my dog), for ensuring the stress free
moments and inspiration.
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Table of Contents
List of abbreviations ……………………….………………………………...…….…....7
List of figures…………………………………………………….………………...........7
List of tables…………………………………………………….……………….…........7
1. Introduction………………………………………………………..................................8
1.1. The Swedish emergency care…………………………………………………..............8
1.1.1. Triage and flow ………………………………………………………............8
1.1.2. Documentation…………………………………………………………..........9
1.2. Data quality………………………………………………………………...……………9
1.2.1. Definition and Impact of data quality……………………………………........9
1.2.2. The Sepsis example………………………………………………….............10
1.3. Problem statement………………………………………………………....….............11
1.4. Aim and objectives……………………………………………………...….…............11
1.5. Research question.……………………………………………………..…….………...11
2. Methods…………………………………………………….……………...……….......12
2.1. Research Methodology and Approach………………………………...……...............12
2.2. Data collection tools…………………………………………………..……................13
2.2.1. Document analysis……………………………………………..……….........13
2.2.2. Study Settings…………………………………...………..............................13
2.2.3. Recruitment of participants……………………..…......................................14
2.2.3.1.
Sampling…………………………………….…………...........16
2.2.4. Interviews……………………………………………………..……………...16
2.2.4.1.
Pilot interview…………………………………..…………......16
2.2.4.2.
Main Interviews……………………………......………...........17
2.2.4.3.
Continuous development and saturation……..………..………17
2.3. Data analysis……………………………………………….………….........................18
2.3.1. Deductive Approach……………………………….…………………...........18
2.4. Ethical Considerations………………………………….……………….....................19
3. Results…………………………………………………….…………………………….19
3.1. Process Results…………………………………….…………………………………..19
3.1.1. General emergency care patient flow and documentation processes…..……19
3.1.2. An insight into three different ED patient flow and documentation
processes……………………………………………………………….…..…24
3.1.2.1.
ED paper based documentation process………….....................24
3.1.2.2.
ED digital documentation process………………………...…....25
3.1.2.3.
ED paper and digital documentation process……....................26
3.2. Conceptual analysis from the interviews………………………………………..,,,,,,,,..27
3.2.1. Coding and main concepts…………………………………………...….........27
3.2.2. Data analysis and the adopted data quality framework.………………………35
4. Discussion……………………………………………………….……………..……......37
4.1 Patient flow, documentation processes and staffs insights on data quality in the
Swedish ED …………………………………………………………………………38
4.1.1 Standardization of Processes……………………………………………….....39
4.1.2 Interoperability of IT systems, Education and Competence……………….....41
4.1.3 Management…………………………………………………………………..42
4.1.4 User need for System Support………………………………………………...43
4.2 Methodology considerations…………………………………………..……..…......45
4.2.1
Limitations and strengths of the study…………………..………………….45
4.2.2
Trustworthiness, credibility and dependability…...……………………… 46
4.3 Implications of this study: Recalling the sepsis example.……………………........46
4.4 Future research…………………………………...………………………………...47
5. Conclusions………………………………………………....…...................................48
References………………………………………..……………...................................49
Appendices………………………………..…………………………………………….52
Appendix A –Main interview guide (in Swedish) ……..……..………….....52
Appendix B – Patient flow and documentation processes for settings
A;C;D.……………………………..……..……………….......53
Appendix C- Mind map of data analysis code results...………….………….56
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Abbreviations
CDSs- Clinical decision support systems
ED- Emergency Departments
EHRs- electronic health records
MD- medical doctor
RN- Registered Nurse
VS- Vital signs
List of figures
Figure 1- The research Stages.
Figure 2- “Zigzag Data Collection and Analysis to Achieve Saturation of Categories”, by
Creswell, 2008 (38).
Figure 3- The patient flow process.
Figure 4- The re-evaluation at the ED.
Figure 5- Patient flow and documentation processes at an ED with paper based
documentation.
Figure 6- Patient flow and documentation processes at an ED with digital documentation
process.
Figure 7- Patient flow and documentation processes at an ED with paper and digital
documentation process.
Figure 8- Diagram of codification results after interviews analysis.
Figure 9- Occurrences of the two major themes.
Figure 10- Occurrences of the three main categories of the human factors theme.
Figure 11- Occurrences of the categories and sub-categories created of the human factors
theme.
Figure 12- Occurrences of the two categories created and included in the technical theme.
Figure 13- Occurrences of the categories and sub-categories coded for the technical theme.
Figure 14- The relation between the 3 dimensions for data quality and the code generated
from the data analysis of the study interviews.
Figure 15- Most influencing factors of the three data quality dimensions.
Figure 16- The Emergency Department cycle.
List of tables
Table 1- Description of the study settings.
Table 2- An overview of the study participants.
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1. Introduction
1.1 The Swedish Emergency Care
Emergency care is a central piece within healthcare and includes all treatment provided in
declared medical emergency situations(1). Sweden’s national government is responsible for
overall healthcare processes and policies(2) and each one of the Swedish County Councils is
responsible for ensuring universal access to good healthcare to the citizens(1)(3).
Over the last few years emergency care in Sweden went through intense reforms and
restructuring that allowed the healthcare sector to offer improved quality and results while
serving an expanding, ageing patient population (4). Improvement policies in Sweden focused
on consolidation, differentiation and specialization, and that is why nowadays, emergency
care is no longer exclusively associated with hospital settings but can also be provided by
different settings such as open-clinics (1)(4). According to an official report 2,4 million visits
to Swedish hospital EDs occurred only during 2013(1). It is therefore important, to have
strong organizational and structural policies in order to ensure that care is provided with the
highest quality. In emergency care one of the ways of sorting or selecting the patients is by
using triage, a process described below.
1.1.1 Triage and Patient Flow
Triage is a dynamic decision-making process that prioritizes a persons need for medical care
prior to or on arrival at an ED (5). Triage has been developed since the 1990´s and is
nowadays broadly used in several different countries(6). Triage includes the analysis of
emergency symptoms and signs with special attention to detect risk factors related to
objective physiological vital signs (VS), vital history and risk factors (7). Triage is often
carried out by registered nurses (RN) that use a guiding triage scale to help in the decision of
allocating a patient to an acuity level (8). However in Sweden, the degree of medical urgency
can also be assessed by a care team involving different staff categories such as physicians,
nurses, nurse assistants and secretaries (9). Triage represents only a part of the patient´s
emergency care and can be seen as the primary step into the ED. Triage scales can be used
partially to perform an “outer triage” upon patient´s registration at the ED that is followed by
a later “full triage” or physician´s assessment (10).
In Sweden three triage methods are commonly used, the Medical Emergency Triage and
Treatments System (METTS), Adaptive Process Triage (ADAPT) and the Manchester Triage
Scale (MTS)(9). According to a 2010 report, referring to 2009 numbers, one third of the
Swedish hospitals uses METTS, one third uses ADAPT, and one third uses MTS or locally
developed triage scales(9). The same report shows that scientific evidence is insufficient to
appraise the validity of the different triage scales when regarding their capacity to accurately
predict a clinical event that requires the immediate physician´s attention (8). Both ADAPT
and METTS are process-oriented triage scales and the development of these scales has
preceded the development of patient flows in the Swedish ED(8).
The work of processing the patients in the ED, with the aim of speeding up patient throughput
is called flow processes. Different flow processes are in use in Sweden today, depending on
the hospital organization and management. Flow processes are used in the ED to organise
routines and contribute to reduce waiting times and overall length of stay(9). They are an
efficient strategy to reduce variability in care and ensure that patients receive standardized
care that has been shown to improve the outcomes (11). An example of a flow process within
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ED is the “fast-track”, where a special coherent process is used in order to manage patients
with minor disorders or injuries (9), allowing patients to “flow” within the ED in a smooth
pre-defined and efficient way.
1.1.2 Documentation
During ED triage information about the patient´s clinical status, such as vital signs, is
collected and measured and needs to be introduced in to the patient´s EHR, in order to be
used, documented and stored.
At the ED, there is a need for secure and accurate transfer of information between different
caregivers (12), which means that documentation in patient records is a priority. Since
ineffective transfer of information and communication between healthcare professionals has
been reported to be a vulnerability of the triage process(13), it is crucial to ensure that a
correct and complete documentation of the triage moment, and specially vital signs
assessment is performed.
The patient record is the main source of information regarding patient´s healthcare history.
Swedish regulations state that the procedure for documentation of patient data must ensure
that the patient record can provide a ground for monitoring health care and it´s quality (7). It
is known that, by working in adherence to the implemented triage system, it is more likely to
achieve a higher level of correctness in documented triage level, which will also improve
patient safety and even increase cost effectiveness (7). Patient safety is therefore strictly
connected to triage compliance and good documentation routines (14). Documentation and
the use of common triage systems and terminology is essential throughout the healthcare
chain (7). Adherence to triage systems by measuring all vital signs is important to be able to
document the correct triage level of a patient (15). Neglect to comply with triage
documentation can result in a negative outcome for the patient´s health (16).
In Swedish EDs triage documentation is performed by using EHRs and/or paper based forms.
EHRs, in particular, have potential to transform healthcare systems by allowing clinical data
to be transferred, stored, analysed and exchanged in an efficient way(17). But EHRs are
highly dependent on the quality of the documented data to have that transformation potential.
It is therefore important to gather more knowledge in the process of collecting and
documenting data within the ED and the factors that might influence those processes. Both
clinical and documentation processes within the ED have therefore to be understood and
studied.
1.2 Data Quality
1.2.1 Definition and Impact of data quality
Quality of data can be evaluated by using both qualitative and quantitative evaluations(18),
and it can be used as a quality of care measurement. In Sweden both official inspections and
filed complaints within healthcare, often show poor or insufficient documentation within
several healthcare departments (19). Inaccurate data may lead clinicians to make treatment
errors(20), researchers to underestimate disease prevalence(21), health-system managers to
underestimate compliance with care standards and alerting systems to send false alarms to
physicians (22). Clinical data, or information recorded in the patient health records, needs to
have quality in order to be able to be transferred, communicated and reused in a trustful way.
Reuse of information recorded in the patient’s health record, is important not only while
patient´s receive care at the ED, but also in a later stage during other healthcare related
contacts. Also, the retrospective use of health data holds the promise to expedite scientific
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discovery (23), but currently secondary use of clinical data is still at an early stage (24).
Despite the well-known benefits of EHRs, reuse of data has been limited by several factors
including concerns about the quality of the documented data (18).
Quality has been defined through the “fitness for use”(25) which, in the context of data
quality, means that data is of sufficient quality when it serves the needs of a given user for a
specific goal(18). In the ED setting it would mean that information or data recorded in the
patient records serves the purposes needed, such as making clinical decisions, treatment
planning and execution (14). Few studies reflect on how quality of data can be achieved, but
in the nursing field, several studies provide recommendations that facilitate nurses
documentation strategies(26). However, the impact of those changes and recommendations in
the quality of data is not known.
The framework for data quality analysis used during this study was the one proposed by
Weiskopf and Weng in 2013 (23). The authors aimed for the reuse of the EHRs data for
research and proposed five dimensions of data quality: completeness, correctness,
concordance, plausibility and currency. These five dimensions were defined by the authors in
the following way (23):





Completeness- referred to whether or not a truth about a patient was present in the
EHR; this dimension aims to ensure that all information collected from the patient is
present in the EHR;
Correctness- data in the EHRs was considered correct when the information they
contained was true;
Concordance- concordance exists when there is agreement or compatibility between
data elements;
Plausibility- data was considered plausible if they were in agreement with general
medical knowledge or information and were therefore feasible;
Currency- It refers to the timeliness or recency, in the EHRs data is considered current
if recorded within a reasonable period of time.
In order to better understand the importance of correct documentation and EHRs data quality
upon triage at the ED, an example is provided below.
1.2.2 The Sepsis example
Sepsis is an infection based systemic inflammatory response syndrome that is clinically
classified in three stages: sepsis (primary symptoms of the disease), severe sepsis (associated
with organ failure hypotension or hypo perfusion) and sepsis shock (the most severe clinical
stage of sepsis characterized by hypotension that persists despite adequate intravenous fluids)
(27). Sepsis can be either a primary or hospital acquired infection, and it was pointed out as
one of the major avoidable hospital related health complications in Sweden in 2013 (28). Due
to the quick progression of the disease and in order to increase the survival rate for Sepsis
patient´s, it is extremely important to perform an early diagnose where early inspection of
vital signs is not only a quality factor for the measurement of healthcare quality in Sweden,
but also a necessary step upon triage (27)(29)(30).
Even if documentation of vital signs is one of the most commonly performed tasks in EDs
(30), statistics show that in the year of 2011 29% of the Sepsis diagnosed patients missed any
register of a specific vital sign, breathing rate, in their medical records (27)(31) a trend that
continued through 2013 (32). Several other studies suggest that vital signs documentation is
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commonly incomplete (33)(34). In the particular case of sepsis, the fact that vital signs
information either is missing, incomplete, or not documented in the patient record, means that
vital signs data in the patients EHRs is missing quality. This fact can have a big impact in the
medical outcome and assessment of this particular group of patients. Since this disease
progresses in a very quick way, it is fundamental to detect vital signs alterations on time in
order to improve the Sepsis patient´s clinical outcome.
This example raises questions on both the documentation routines or processes and the quality
of the documented patient data, specifically vital signs, upon triage in the Swedish EDs.
1.3 Problem Statement
Swedish EDs follow different triage protocols in order to sort, prioritize and select patients in
to a specific patient flow process aimed to improve healthcare delivery in both quality and
time. Documentation is performed throughout this process but, even if the different triage
processes are well structured and defined, the way patient related information is collected,
communicated and recorded in the patient´s record is pointed out as a feasible reason for
many of the healthcare reported errors and complaints filled to the Swedish National Board of
Health and Welfare (19). Furthermore, the factors affecting vital signs data quality during the
triage process and the quality of the EHRs data per se are unknown. As stated before, good
quality documentation of triage results and contents, such as vital signs, is essential for both
patient safety and quality of care. There is therefore an interest in knowing which factors can
affect the quality of data recorded in patient records upon triage. From a health informatics
point of view, data quality affecting factors, have to be known so improvements can be made
in the quality of data, before it can be reused in clinical decision support systems (CDS),
research or quality management within healthcare. Data reuse within healthcare may then be
improved if data is collected and documented with quality.
1.4 Aim and Objectives
The aim of this study is to explore the factors that affect data quality during triage in EDs.
The first objective of this study is to describe documentation processes and patient flow in the
Swedish ED. This will evaluate if there is a relation between documentation processes and
data quality and increase the knowledge about the factors that may affect data quality in the
Swedish EDs.
The second objective is to collect ED staffs perspectives regarding the quality of data
documented in the ED. A description of barriers and facilitators in documentation processes
that affect data quality is aimed as well as the link between barriers/opportunities and data
quality.
1.5 Research Question
This study aims to answer the following question:
Which factors impact data quality in the Swedish Emergency departments?
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2. Methods
2.1 Research Methodologies and Approach
In order to achieve the aim and objectives of this study and answer the proposed research
question, a qualitative study was performed. The choice of performing a qualitative study is
due to fact that, even if data quality can be quantified, the factors that have impact on the
quality of data during the documentation process at the ED, are hard to quantify. These
factors are linked to organizations and individuals that will be evaluated in perceptions and
experiences, not quantifiable in a quantitative way.
During the first phase of this study meetings were held with Dr. Niclas Skyttberg, a clinician
and expert in triage and medical management as well as Prof. Sabine Koch and Dr. Rong
Chen, experts in Health Informatics. During these meetings in depth knowledge of the triage
protocols, documentation and variations in Sweden was provided and gathered. Parallel to this
introduction phase an extensive literature research was performed in both triage and data
quality. In order to be able to study the impact of different factors in data quality within the
Swedish EDs, a focus was set in the vital signs collection and documentation as part of the
triage protocols. By focusing on the vital signs data collected, documented and recorded in
Swedish EDs, a narrow specific section of the triage process was selected in order to be
analysed and lead to the aimed answers. As mentioned before, Weiskopf and Weng in 2013,
proposed five dimensions for EHRdata quality assessment: completeness, correctness,
concordance plausibility and currency (18). In order to be able to analyse the quality of data in
the documentation of vital signs within the triage process in Swedish EDs, and for the scope
of this study, three data quality dimensions were chosen: correctness, completeness and
currency. Both plausibility and concordance were considered a part of the correctness
dimension and therefore not specified for this study.
These three data quality dimensions were used in the second phase of this study, where
interviews took place, for both the development of the interviews questions and the analysis
of the interviews data. Interviews were held with ED staff members of bigger hospitals in the
Svealand and Götaland region in Sweden. A pilot interview guide was developed from the
three selected dimensions for data quality that were the base for a range of in depth questions.
This guide was tested in one interview and led to the final interview guide that was organised
in to three sections, each one focused in one of the three mentioned data quality dimensions.
That is, the final interview guide questions were built to gather information on how each one
of the data quality dimensions is thought to be affected at the ED. Interviews were performed
in a structured way in several different settings and the collected data was analysed by
constant comparative analysis as described by Strauss and Corbin 2008 (35). During the
interviews data analysis the chosen data quality dimensions, were once more used in order to
compare the coding results of the interviews with the framework for data quality. Data
analysis was performed by using the deductive content analysis method (23) in which the
content of the data analysis is used to gain a logical support to the findings and confirm
eventual previous knowledge (36). Inductive data analysis methods were also used in order to
claim that some of the findings were probable or likely to be found in the ED setting.
Data gathered from the interviews was also used during the third and final study phase, where
the description of specific patient flow and documentation processes, for each one of the
studied hospitals settings and interview data analysis, occurred. Data from the interviews was
12
enriched by the analysis of specific EDs documentation such as paper based triage forms. A
mapping of the patient flow and documentation processes for different settings was created in
order to analyse and compare documentation strategies between them. The VUE(42) tool was
used to draw the patient flows and documentation processes for each one of the clinical
settings. These patient flow and documentation processes were cross checked with official
reports gathered during literature research(10,37). A general patient flow and documentation
processes were then extrapolated, representing the general processes that occur in Swedish
EDs. This generalisation of processes allowed a better and graphical analysis of the factors
that influence the quality of data in the ED.
An overview of the research stages of this thesis is provided below.
Figure 1. The research Stages.
2.2 Data collection tools
2.2.1 Document analysis
Both official triage paper forms and reports from the emergency care unit from the different
hospitals were used in order to collect information regarding the patient’s flow through the
ED and the staff’s documentation process. These documents included official triage and ED
patient forms that allowed the researcher to collect information on how vital signs data are
measured, documented and used during before and after triage. An official report (10) on
some of the EDs from the Svealand region, allowed the verification of the patient and
documentation processes designed for those settings. Findings from this document analysis
will be presented later on with the processes description during the results section.
2.2.2 Study Settings
In order to collect information onsite, several emergency care departments of major hospitals
in Sweden were selected. The clinical advisor of this study, Dr. Niclas Skyttberg, was the
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main source for these hospital contacts. The settings were selected in first place by their
geographic location around Stockholm, in the Svealand region, in order to allow face-to-face
interviews. However, some hospitals settings were located in the Götaland region, requiring
interviews to be performed by telephone. This different location allowed the study to not only
compare results between regions but also discuss if factors that influence the quality of data in
the EDs, vary or not according to the setting or geographic location.
A total of eight different EDs from different Swedish hospitals were selected to participate in
the study. A brief description of each one of the settings is provided below.
Table 1. Description of the study settings. n=8.
Setting
Location
Patient intake
80 000 patients per year,
Svealand
Hospital A
excluding the gynaecological
Region
emergency patients
Svealand
73 000 patients per year also
Hospital B
Region
excluding gynaecological patients
Svealand
75 000 per year within 8 different
Hospital C
Region
medical specialities
Description of the ED
It is a general ED with specialisation in
intern medicine, cardiology, surgery
and orthopaedics (10)
It is a general ED with 9 different
medical specialities as well as a
regional specialised trauma centre (10)
Hospital D
Svealand
Region
73 000 patients per year
The hospital is specialised in intern
medicine, surgery and orthopaedics.
(10)
Hospital E
Götaland
Region
Together with another major
hospital this setting treats around
400 patients per day (40)
Standard ED
42 000 patients per year
Standard ED (10)
34 000 patient per year only in
the emergency department
One of the 20 main regional hospitals
in Sweden. (41)
Provides specific emergency
departments for children, infectious
patients, gynaecology and psychiatry
(44).
Hospital F
Hospital G
Götaland
Region
Götaland
Region
Hospital H
Svealand
Region
53 000 patients per year
Hospital I
Svealand
Region
During 2013 it received and
treated 42 702 at the ED(1).
Standard ED.
2.2.3 Recruitment of participants
Because this was a qualitative study, participants were chosen by “purposeful sampling”, that
is, since the intent was not to generalize to a population but to develop an in-depth exploration
of a central phenomenon (38), in this case factors influencing vital signs data quality in the
Swedish EDs, we purposefully selected individuals and sites. The standard used to select
participants was whether they are “information rich” (42) or not. Participants were then
recruited from all the presented hospital settings.
Hospitals settings were approached via email sent to either the chief nurses department or
physicians working on site. The email explained the purpose of the study and asked for
participants that had experience in ED triage, triage education, quality control or
administration. Since triage in Sweden is mainly performed by registered nurses (14), we
asked for the participants to be RN but two of the interviewees were MD.
It was important to select participants that had experience of working with triage at the ED,
since their clinical experience and knowledge was considered a main knowledge source for
triage and vital signs measurement and documentation processes. RN with experience in
triage education were a valuable source in order to evaluate and obtain an in-depth sight of the
14
way triage methods are taught in the ED. Participants with leading roles in healthcare quality
evaluation and administration, with combined clinical work, were also considered since their
knowledge in healthcare quality factors and ED performance provided an insight into the
actual status of the quality of data registered and collected at the ED.
A total of sixteen professionals were interviewed, two medical doctors and fourteen registered
nurses. An overview of the interviewee’s profession, clinical role at the ED and experience is
presented in Table 2 below.
Table 2. An overview of the study participants. Their role at the ED, clinical experience in years, experience in
the ED in years and clinical workload. NA- information not available. n=16.
Interview
Number
Setting
Number
Interviewees
Profession
Clinical
Experience
in years
ED
experien
ce in
years
% of
clinical
work
Medical Doctor (MD)
NA
NA
50
9
9
60
NA
NA
NA
1
Hospital E
1
2
Hospital D
1
3
Hospital F
1
4
Hospital C
1
RN, Healthcare
quality responsible
More than 10
10
50
5
Hospital D
1
RN
More than 18
18
100
2
RN and responsible
for triage education
at ED
More than 17
17
50
RN at children´s ED
9
9
100
RN specialist in
emergency care
4,5
1
100
RN and ED
education leader
24
18
0
20
11
50
27
27
50
6
7
8
Hospital C
Hospital B
Hospital A
1
2
Chief RN at ED and
ED healthcare
developer
MD
9
Hospital G
1
RN responsible for
quality of care
development
ED chief RN
10
Hospital H
1
ED chief RN
27
21
11
Hospital G
1
RN
More than 15
15
100
12
Hospital I
1
RN
More than 13
13
100
13
Hospital G
1
12
26
50
14
Hospital H
1
10
16
50
RN, worked before
as an ED
administrative
RN
15
All participants were contacted by email and either a face-to-face or a telephone interview
was scheduled accordingly. The pilot interview was a “test-up” interview that allowed the
testing of the interview guide for the aim of obtaining the pretended information. The pilot
interview was performed face-to-face with one RN participant and lasted around 40 minutes.
The pilot interview guide was then restructured and used in all the remaining 13 interviews
with a new improved guide, structure and set of questions based in the data quality framework
dimensions considered for this study. The main interviews lasted no more than 30 minutes
and were performed either face-to-face or by telephone.
2.2.3.1 Sampling
Sampling can be defined as a process or method to select a part of the population, or sample,
in order to determine characteristics of the entire population (38). There are different types of
sampling and two were used in this particular study: theory or concept sampling and
homogenous sampling. Theory or concept sampling was used in order to help the researcher
sample individuals because they can help the researcher generate or discover a theory or
concepts within a theory (42), in this case factors affecting the quality of data at the Swedish
ED. Homogenous sampling was also used since the researcher purposefully sampled
individuals based in a subgroup with specific characteristics (42), that is all the participants
needed to work within ED, have experience in triage methodology and even some other
responsibility connected either to ED education or healthcare quality administration.
2.2.4 Interviews
Interviews were the main data collection tool selected for this study. Semi-structured
interviews are pointed as one of the best methods to collect and try to understand information
from other human beings (38). This type of interviews allows researchers to address issues of
trust and organization, while maintaining a feeling of openness (39).
Interview open-ended questions were developed in a pilot interview guide that was tested
during one pilot interview. This guide was then developed to a new range of questions that
were used for both face-to-face and telephone interviews. Each interview was performed in
Swedish, audiotaped, with the consent of the interviewee, and transcribed verbatim in order to
facilitate the data analysis. Some of the interviews were performed with the great support of
Dr. Niclas Skyttberg, and only I conducted others.
2.2.4.1 Pilot interview
A preliminary interview guide was developed, using the three dimensions for data quality:
correctness, completeness and currency. This guide aimed to collect information about the
triage process, vital signs measurement and documentation and the interviewee own
perception on triage, the documentation strategy in place and the quality of vital signs data
collected and registered on ED patient records. This preliminary interview guide was tested in
a pilot-interview that allowed the testing, restructuration and narrowing of the interview
questions. This step was fundamental in order to both reduce the amount of questions and
narrow the collected information by using more specific questions.
The pilot interview was performed with a RN in a face-to-face interview. This interview was
performed in 40 minutes and gave an overview and broad insight on both triage and vital
16
signs measurement and documentation processes. This interview was performed in Swedish,
audiotaped and later transcribed in verbatim.
2.2.4.2 Main Interviews
A new interview guide with a new set of questions was developed from the experience
gathered with the pilot interview. This new interview guide (available in Appendix 1), was
also divided in main sections: ED vital signs measurement process, documentation process
and paper based documentation.
Questions related to the ED VS measurement process aimed to have the interviewee describe
the triage process in use at that specific hospital, what can determine if a triage process is
complete or incomplete and the stakeholders opinion on if patients that are not submitted to
triage are healthier than those that are. Questions related to ED documentation processes
pretended to collect insights and information related to the way both triage and vital signs are
documented in that particular setting. Specific questions were introduced in this section of the
interview guide regarding the three foci of this study: correctness, completeness, currency, as
well as the factors that the interviewee though could influence these three data quality
dimensions. Finally, a question was introduced in the end of the interview guide to collect
information and insights on the eventual use of paper-based documentation.
A total of 13 interviews were performed both in face-to-face and telephone and lasted no
more than 30 minutes. All study settings were represented with at least one interview. All
interviews were performed in Swedish, audiotaped and later transcribed in verbatim in order
to facilitate the on-going data analysis.
2.2.4.3 Continuous development and Saturation
While the interviews were being held, data analysis of the interviews was being performed in
parallel using an emerging design. An emerging design is a process in which data is collected
and analysed immediately, rather than waiting until all data collection is achieved (38).
Continuous analysis, development and improvement of the collected data were performed
from interview to interview, which allowed the researcher to progress to data saturation.
Saturation was achieved when no major themes and no new information could be added to the
data analysis results (38) from the already performed and analysed interviews. By achieving
saturation with the interviews data, the researcher made the subjective determination that new
data, collected with more interviews, would not provide any new information or insight to the
developing categories (38). Figure 2 illustrates the zigzag data collection and analysis process
proposed by Creswell, 2008 to achieve saturation of categories that exemplified the parallel
data collection and analysis performed during this study.
17
Figure 2- “Zigzag Data Collection and Analysis to Achieve Saturation of Categories”, by
Creswell, 2008 (38)
2.3 Data analysis
2.3.1 Deductive approach
A common, widely used method for qualitative data analysis (45) results from a systematic,
qualitative study procedure that is used to obtain a theory. That theory explains, in a broad
concept level, a process, action or interaction about a specific topic (38). In this particular
study, by using this approach it is possible to start the study without predefined theories and
to build theories and deep explanations and descriptions of the factors affecting the quality of
data today in Swedish EDs. Since a broader explanation of the impact of the documentation
processes in the quality of data is aimed, and few studies evaluating the factors that affect
quality of data in ED exist, the use of this method for qualitative data analysis is the optimal
tool to analyse the data collected during the study.
As mentioned before, rather than beginning by researching and developing hypothesis the
researcher´s starting point was the data collection through the use of interviews. A
comparative data analysis was performed in order to move back and forth among the data
while it was being collected, as suggested by Strauss and Corbin 2008 (35). The aim of the
study was to identify factors affecting data quality, so a deductive content analysis was also
used, in which the structure of data analysis is operationalized based on previous knowledge
and the purpose of the study is theory testing (36). A deductive approach is based in an earlier
theory or model and therefore moves from general to specific (27). In this particular study, as
mentioned before, the framework for data analysis is the one proposed by Weiskopf and
Weng in 2013 (23). The researcher departed from this framework and after making sense of
the collected data conducted a data analysis using a deductive approach.
The theoretical framework for data quality was used in this study (23) throughout the methods
and the results section and worked as a constant resource for development, collection and
interpretation of the study results.
The interviews were analyzed using thematic content analysis to collect staffs perspectives on
data quality at the ED of the different study settings. The interviews, which were conducted
with registered nurses with different functions within the ED, and two medical doctors,
allowed the researcher to collect information on how ED staff thinks quality of vital signs
data recorded in the patient´s record is today and why. Perspectives and insights were
collected on how completeness correctness and currency of vital signs data might be affected
at the ED today and why.
After transcribing the interviews in detail a codification process was performed. Codification
18
was made at the same time that the interviews took place and both themes and concepts were
constantly reviewed, generated and checked. Since the interviews were performed in Swedish,
codification and creation of the different categories was performed in English language in
order to allow a better presentation of the thesis findings. The process used to perform the
codification of the interviews text was based in a deductive method. Information contained in
main or key interview text sentences was classified and deducted in to themes, categories,
sub-categories and concepts. The study participant´s insights were classified from themes to
concepts based on the research question (which factors impact data quality in the Swedish
EDs) and the adopted framework for classification of data quality.
2.4 Ethical Considerations
Ethical approval was applied for at the Stockholm representation of the Swedish Central
Ethical Review Board (Regionala Etikprövningsnämnden i Stockholm) which decided that an
ethical approval was not necessary to perform the study.
All participants were fully informed about the goals, aims and objectives of this study. They
were also informed about the anonymity of the collected data in order to ensure protection of
their integrity.
Interviewees voluntarily participated in the study and gave verbal consent prior to the
audiotape of the interviews.
3. Results
This section will present the results and finding of this study. Results are presented in two
sections: the patient flow and documentation processes results, named process results, and the
conceptual analysis results from the interviews.
3.1. Process Results
The aim of this study was to explore the factors affecting data quality by achieving the
following objectives: describe documentation processes and patient flow in the Swedish ED
as well as to collect ED staffs perspectives on the quality of data documented in the ED. In
order to achieve the first objective information was gathered from both interviews and local
documentation on triage forms and ED routines documents. Information collected from these
sources was then compared to official reports on both triage and ED organization (10)(8)(1).
This information led to the creation of two different types of processes: the ED patient flow
and the ED documentation. The general patient flow and documentation processes for the ED
are presented below. Study results provided a deeper knowledge on three different ways of
supporting documentation at the Swedish EDs today: digital, paper or both.
3.1.1 General emergency care patient flow and documentation processes
Both interviews and local documentation, such as triage and ED registration forms, provided
a good understanding about the way patients visit to the ED is organized and how
documentation is performed on the way. All ED settings considered for this study had a
similar organization and structure. However, differences were visible not only on how triage
was performed, but also how vital signs information was measured and documented.
19
Patient flow
The way patients move through the ED ward differed from setting to setting according to
local routines and organizational strategies. The way a patients visit is organized and
structured within the ED can be referred to as a patient flow process. This flow is deeply
connected to the way documentation of the patients visit at the ED is performed by staff. That
is, while the patient moves through the ED, information is collected and needs to be
documented. One can think of two parallel processes that simultaneously take place at the ED,
the patient flow and the documentation processes. Interviews showed that the way of
performing ED documentation is extremely variable from site to site, but in the end the aim is
to document and make information collected and generated during an ED episode, accessible.
In order to describe the patient flow and documentation process at the ED, a general
description of both processes is provided below
The patient flow was generalized to the one presented in figure 3. The main steps in the
patient flow process are arrival, registration, triage, monitoring and treatment, decisions and
discharge. The figure provides an overview of this process together with the main clinical
steps and documentation moments that happen at the same time while the patient flow process
took place.
Figure 3. The patient flow process.
Figure 3 represents the main clinical and documentation steps at the ED. The vertical oriented
process on the left illustrates the patient flow process through ED from start to end. The two
horizontally oriented processes represent the clinical steps that occur during the patient´s
process and the main documentation support used at the ED.
20
Arrival and registration
Patient´s arrival to the ED was marked by one of three methods: registration in a specific desk
(together with a primary sorting); together in combination with triage or by the use of a
queuing system without sorting. All hospitals performed patient registration and this was the
first step for all patients arriving at the ED on their own, without ambulance.
Registration performed through desk was described in some hospital settings as a way of
sorting the patient inflow to the correct ward. This was particular important since those
hospitals had a minor ED, or “lättakuten”, responsible for the care of considered minor
emergency situations. This primary sorting was based on a pre-triage. The desks were used to
classify patients and forward them to the correct emergency ward in avoiding queuing and
long waiting times at the “main” ED. Desks were available during the day but their use was
limited during night time. Other settings performed patient registration upon triage that is the
patient was admitted to the triage team and registration performed at the same time. Queuing
systems, where patients collect a number and wait for their turn, were used in one hospital.
There patients were admitted at once to the ED team, without needing to register or perform a
pre-triage step.
If patients arrived by ambulance registration, and sometimes triage, was not required since it
would already have been performed by ambulance staff. For the scope of this study patients
arriving via ambulance will not be considered in depth.
Triage
Once the patient was admitted / registered, triage could start. Triage was described in a
similar way in all study settings. It included several clinical steps such as patient history
taking, clinical measurements, communication and documentation that resulted in the end in a
priority scale result that classified the patients according to their need for emergency care
treatment. International triage scales such as METTS and ADAPT, were used in all sites in
order to prioritize patients upon triage. Triage scales were not used during pre-triage, where
the patient only described the actual symptoms and a decision tool was used in order to sort
patients to the correct ED ward. Triage included, patient history taking with collection of new
information used to update or create a patient EHR. Information considered relevant such as
allergies, previous medical care and medication was collected together with patient´s
description of symptoms that led to the actual visit to the ED. Clinical measurements follow
this first triage step and are performed accordingly to the information collected previously.
Vital signs measurements are here performed, communicated to the patient and other staff
members and documented in the patients record. Vital signs include, blood pressure, heart
rate, breathing rate, temperature and conscience level. According to the Swedish triage
recommendations (9), all vital signs have to be measured and documented during triage. Even
if this is the rule, in particular cases such as orthopedics patients and children, patients
admitted to the ED were reported to go through triage without measurement and
documentation of all vital signs. This decision to not measure all vital signs upon triage was
associated with factors described further on the discussion section of this study. Triage was
performed, in all studied settings, by registered nurses that either worked alone or in two
teams. Triage was concluded by a scale result that classified the patient according to their
emergency.
Triage was also the first main documentation step within ED. The way triage was documented
by staff at ED varied a lot from one study setting to another. Some settings based the ED
documentation in a paper based form. This form provided a structured way of collecting all
21
information needed to perform patient´s triage, monitoring and follow their treatment at the
ED. It was based on tables and charts that were filled in directly after the collection of the
information. The paper form followed the patient through all time at the ED and was
constantly updated when new information on VS or patients clinical status was collected.
Settings using the paper based forms mentioned an instant documentation of information
directly after collection. For example, when vital signs were measured results were
documented instantly on paper. When this paper based documentation was used during all
the process, the paper would either follow the patient to the next hospital ward, or be saved
and later on scanned into the EHR, if the patient was discharged home.
Other settings adopted a digitalized documentation policy, where information was collected
and inserted directly into the EHR. In order to achieve this digitalized way of documentation
different strategies were mentioned during the interviews. Some of the settings performed
triage in special triage rooms with own computers, where measurements and information
could be collected by the registered nurses and immediately inserted and documented into the
EHR, with no need for delays in documentation. However, in case of high patient inflow, a
mixed documentation strategy could also be used. That is upon triage and when no computers
were available to use at the time, or when the amount of patients didn’t allow access to the
computers, paper-based documentation would be performed in order to register patient´s
information in a quick and easy way. That paper would be used by staff until there was a
chance to document the information in the EHR, later on.
Specifically with vital signs measurements these settings described that VS would be recorded
on paper, and later on introduced into the EHR. The time slot between measurement and
documentation of vital signs into the patient´s journal was reported to vary a lot between 5
minutes to several hours, depending on multiple factors presented later on this study.
Monitoring and treatment
When triage was concluded the patients entered the next phase in the ED patient flow process:
monitoring and treatment. This was the actual treatment phase where medical treatment was
provided and monitoring of the patient allowed staff to supervise their clinical status. During
this phase, vital signs measurement, a step called re-measurement of VS, could happen in
order to monitor patient’s condition. However, not all vital signs needed to be re-measured
and the selection of the vital signs that needed to be followed during the monitoring and
treatment phase was associated to the patient´s individual clinical status. Re-measurements
were also mainly performed by registered nurses. Documentation of information collected in
this stage would follow the documentation pattern adopted at previous step, triage. However,
even if documentation was performed by either paper or digital documentation, when
considering vital signs re-measurements, they might even not be recorded or documented in
some settings. Patients arriving at the ED via ambulance, were admitted directly in this step of
the patient flow process, since triage and registration, as mentioned before, had already been
performed by ambulance staff prior to arrival at the ED.
Decision point
The next step in the patient flow process was a decision point where patient´s clinical
condition was analyzed and a decision made, by medical doctors, on whether the patient could
be discharged from the ED or not. This decision was based on information gathered all the
way through the actual emergency episode, documented in either paper, digital or both and
resulted in a decision to maintain the patient under motorization and treatment or to proceed
to discharge from the ED. If patient´s clinical condition required further monitoring, the
22
decision to discharge them to another hospital ward was taken. If the patient was considered
well enough a decision would be made to discharge and send the patient home. The patient
flow process ends in this way with the final discharge decision. This decision was also
communicated and documented, representing both the final clinical and documentation steps
in this process.
Re-Evaluation
As mentioned before both patient and documentation processes occur at the same time at the
ED meaning that during the ED processes, events occur based on special actions and main
decisions. Information collected from this study interviews revealed that in some particular
cases, such as high patient inflow at the ED, patients would go through triage, face a waiting
time and needed to be re-evaluated in a second triage step in order to access if their condition
had deteriorate or not during the waiting time. During this re-evaluation process all or some of
the patient´s vital signs would need to be re-measured and documented in order to detect if the
previous triage priority result had changed. A deeper overview of the ED patient flow and
documentation processes when vital signs re-evaluation is needed is presented in Figure 4.
Figure 4. The re-evaluation at the ED. Re-evaluation after triage might occur upon long ED waiting times.
The first horizontal row in Figure 4 shows the ED patient flow process with patient arrival,
triage and re-evaluation. Triage is, as mentioned before a combination of history taking,
measurement, communication, documentation and final priority scale. The second row shows
that triage documentation could be performed with paper or digital support. However, as the
last row shows, even if paper was used to document triage and triage results, and later on
introduced into the EHR, that does not mean that previous steps, such as registration of the
patient arrival, were not performed with a digital information system. Depending on the
23
hospital, documentation was performed in paper, digital or both. The main events that
accompany the ED re-evaluation process were the arrival of the patient at the ED, the
handover to the triage team, the triage moment and a new handover prior to re-evaluation and
new triage. From a clinical point of view, actions occurred mainly during and after triage.
Measurements, vital signs documentation, calculation of priority and priority documentation
represented the main clinical related actions during the process. Triage priority final scale was
the main decision point during triage and was supported by the use of specific triage tools and
scales, mentioned before.
3.1.2 An insight into three different ED patient flow and documentation processes
Since both interviews and document analysis from study settings, showed that differences
occur in both patient flow and documentation processes at the ED, an insight of the
documentation process supported by three different methods is provided in this section. A
description of three different hospital settings is provided representing a paper based
documentation ED, a digitalised ED and one that uses both paper and digital documentation.
A description of patient flow and documentation processes, for other study settings is
provided in Appendix B.
3.1.2.1 ED paper based documentation example
From interviews performed with participants of one hospital setting, an overview of both the
patient flow through the ED and how the documentation was performed was created.
The following figure represents the patient flow and documentation processes at this
particular hospital.
Figure 5. Patient flow and documentation processes at an ED with paper based documentation. Grey= self arrival
patient flow; red= ambulance patient flow; blue= documentation process.
Patients arriving at the ED on their own would take a queiuing number and wait for their turn
in the ED waiting room. If the clinical status of one of this patients deteriorated while waiting,
they would enter a “priority lane”, the same used by patients arriving at the emergency
deppartment by ambulance, where they would skip triage and be immediately admited to the
emergency room. Patients were afterwards transferred to one of the ED section : internal
medicine, surgery, orthopaedics or other for observation and treatment. Patients that would
24
have been waiting at the ED, were admited to a special triage room where triage was
performed and for example, vital signs measured and documented. Patients were then
attributed a triage priority scale result and sorted in to the correct ED section.
When considering documentation, this hospital setting approached a paper based
methodology. That means that all information collected through the patient´s visit at the ED
was documented on a pre-defined paper based triage and ED form that was later on scanned
into the EHR, upon patients discharge. Time for scanning this paper based record into the
EHR could vary between some hours to some days. For patients arriving via ambulance
information was transferred from the ambulance personal to the ED staff verbally, and
immediately recorded on paper, or by using a print-out of the ambulance record. This paper
based ED form was described as an information source that followed the patient throught the
ED.
3.1.2.2 ED digital documentation process example
Interview results from another hospital setting provided information on how this setting used
a hole digital documentation strategy. The way this setting organised the patient flow process
through the ED and the way information was documented at the same time is presented in
Figure 6 below.
Figure 6. Patient flow and documentation processes at an ED with digital documentation process. Grey= self
arrival patient flow; red= ambulance patient flow; blue= documentation process.
Patents arriving on their own at this ED could either be received by a triage team of two
registered nurses or directly by the medical team, depending on how busy the ED was. That is
when there were less patiens and no waiting time, patients were directly forward to the
medical team skipping the triage team. Triage was in these cases performed by the medical
team at the same time that treatment was delivered. Both triage and medical team had access
to own computer stations and performed documentation of information, such as vital signs
measurements, directly in to the patients EHR. For example if blood pressure was measured,
the value was recorded immediately in to the patients electronic heath record. If the ED was
facing high patient pressure, patients would wait for their turn to meet the triage team.
However if their clinical status deteriorated, they would enter the priority lane and, likely
25
ambulance arriving patients, directly taken to one of the emergency rooms for immediate care.
Both ambulance and emergency rooms also performed the digitalised documentation strategy.
Ambulance personal would provide patient information verbally that would immediately be
documented in to the patient record. This ED also had the possibility to directly receive digital
patient ambulance records in to the EHR.
3.1.2.3 ED paper and digital documentation process example
Finally, from interviews performed with participants from a different hospital, an overview of
how the patient flow and the documentation process using sometimes both paper and digital
documentation, was designed. Figure 7 presents an overview for this particular study setting.
Figure 7. Patient flow and documentation processes at an ED with paper and digital documentation process.
Grey= self arrival patient flow; red= ambulance patient flow; blue= documentation process.
Patients arrived at the ED either by their own or by ambulance. Ambulance arriving patients
entered a “priority lane”, where they arrived at the ED and were immediately transferred to
the adequate ED section (ortophaedics, infection medicin). Ambulance arriving patients had,
upon arrival, triage and registration already performed and that information was transferred
to the ED staff either verbally, directly electronically or as a print out paper based form that
was later on scanned into the patient EHR. Electronic transfer of ambulance records was not
immediate, it was described to could take several days to happen. Patients arriving on their
own could also enter this “ priority lane” if , while waiting at the ED waiting room, their
clinical status deteriorated so much that they were considered high priority patients. Patients
arriving on their own and that can wait, are submited to an outer triage performed at a
reception desk where both registration and and symptoms description occurs. Vital signs were
not normally collected in this primary stage in the outer triage. Patients would then be forward
to triage and from there to the adequate ED section. Documentation during outer triage was
performed directly into the patients EHR. Triage results could be documented either directly
into the EHR or in a paper, depending on multiple factors such has high staff workload and
26
computer availability. Information documented temporary in paper was later on introduced in
to the EHR, within a variable time window. Paper based documentation was reported to be
used in rare cases, and the hospital policy and priority was to only use digitalised
documentation strategies.
3.2 Conceptual analysis from the interviews
To collect ED staffs perspectives in the quality of data documented in the ED was the second
objective of this study. Perspectives on data quality were collected through the interviews and
compared to the previously described framework proposed by Weiskopf and Weng in 2013
(23).
3.2.1 Coding and main concepts
From an early stage two main themes emerged from the interviews, while considering the
factors affecting data quality at the ED today: human and technical factors. A mind map of all
the themes, categories, sub-categories and concepts obtained through analysis of this study
interviews is available in Appendix C. Figure 9 provides an overview of these two themes
their categories and sub-categories.
Figure 8. Diagram of codification results after interviews analysis. Two main themes were identified,
human and technological factors. Several categories and sub-categories were described within each theme.
27
During this section quantified results for the occurrences of each one of the coded categories
is used in order to give an overview of the most common or most mentioned codes gathered
from this study interviews. This quantification allowed the author to reflect on the importance
of each one of the code categories regarding their occurrence during the interviews.
Interviews data analysis revealed that human factors affecting data quality at ED occurred in
58% of the interviews data and technical factors in 42%. Figure 9 illustrated the occurrences
of these two main themes.
Factors Associated with data quality at ED.
Technical
Factors
42%
Human
Factors
58%
Figure 9. Occurrences of the two major themes. This graph resulted from the interview data analysis of the
study. Technical factors associated with data quality represented 42% of the data occurrences and human
factors 58%.
Theme 1: Human factors
Early in the process of data analysis of the study interviews, the influence of human factors in
the quality of data at the ED was considered important. The human factors theme relates to
the interviewee’s reflections on the influence that ED staff has in the quality of data that is
collected, documented and used at the ED. The three main categories included in the human
factors theme were the education and competence, the management and the user need for
system support. Those three occurred in different percentages in all 14 interviews according
to Figure 10.
28
Factors Associated with data quality at ED.
Human Factors
Education and
Competence
(57
ocurrences)
21%
User need for
system
Support (168
occurences)
61%
Management
(49
ocurrences)
18%
Figure 10. Occurrences of the three main categories of the human factors theme. The graphic shows the
percentage of occurrences of each one of the categories of the human theme during all the 14 interviews.
Education and competence represented 21%, management 18% and user need for systems support category 61%
of the Humans factors theme.
Figure 11 provides an overview of the occurrences of all the categories and sub-categories
included in the Human factors theme.
Syste
Mana m
Education and geme Supp
nt
ort
competence
Human factors associated with data quality at ED.
Documentation
Workflow
Change Management
Quality control
Experience
Control of staff competence
Slopiness and carelessness
Methodology competence and knowledge
Clinical competence and knowledge
87
81
10
39
15
18
14
10
16
0
20
40
60
Ocurrences
80
100
Figure 11. Occurrences of the categories and sub-categories created for the human factors theme. Occurrences
through all the 14 study interviews.
Education and competence
The education and competence category is associated to the influence of staff´s education and
experience in the quality of data at the ED. This category had 57 occurrences through all 14
interviews and was divided in 5 specific sub-categories that will now be described.
Clinical Competence and Knowledge
During interview number #4 it was stated that “ we record the vital signs values that appear
on the screen, I think that is correct, but I don’t think we always reflect if those values are
29
plausible or not”. This statement reflects on the fact that even if the correct measurements
information is collected and documented, reflection on how those measurements are adequate
to that actual patient or not, may not always happen. Clinical competence and knowledge is
therefore the first sub-category of this theme that defines this reflection and occurred 16 times
through all the interviews.
Methodology Competence and Knowledge
Methodology, competence and knowledge, the second sub-category within education and
competence, specifically relates to the lack of knowledge of error sources described in some
interviews. Interviewee #5 reflected about error sources that could influence data quality at
ED and stated that “During temperature measurement, wax in ear canal may be an error
source”. Interviewee #12 mentioned that “Incorrect vital signs may always occur. Saturation
measurement may be sensible to finger temperature, dirty saturation probe or wrong position
of the sensor”. These two statements represent the influence of the methodology competence
and knowledge of error sources during the practical measurement of the vital signs at the ED.
This sub-category was the one that occurred less in all interviews.
Sloppiness and carelessness
The third sub-category is the presence of sloppiness and carelessness. Interview #2 showed
that “One maybe does not measure pulse rate during one minute and can mix pulse rate with
hearth rate.” Reflections on the presence of sloppiness and carelessness during vital signs
measurements at the ED were present 14 times during all the interviews.
Control of Staff Competence
Interview #6 showed that “We also have a certification procedure for all staff that uses
technical tools, so we can ensure that those tools are not error sources”. This reflection led to
the creation of the fourth sub-category: the control of staff competence that represents
eventual local measures or policies that control staffs knowledge when using technical tools.
Control of staff competence occurred 18 times being the most recurrent concept within
education and competence.
Experience
Finally within this category, experience was described in interview #6 the following way “It
can depend on if you have worked a few years. Not that one is extreme experienced, but if you
have some clinical experience you learn to see if this is plausible or not”. This final subcategory was described 15 times during the interviews.
Management
The management category within the human factors associated with data quality in the ED
theme, had 49 occurrences through all the interviews. This category included quality control
and change management representing organizational policies and strategies present at the
study settings in order to control, monitory and improve quality of care.
Quality Control
Quality control sub-category was represented by 39 occurrences during the 14 interviews. It
30
included reflections on follow up of data quality, adherence to guidelines and discussions on
quality. Follow up of data quality related to interview #6 statements “The only way to prove
this [completeness of patient records] is to make a control check, measure and see, for
example now only 40% of the registers are complete”. Follow up of data quality was
mentioned as a way of controlling data quality at the ED. Discussions on quality and the
follow up the adherence to actual guidelines and triage methods were other two concepts
included in the quality control sub-category. Interviewee #1 mentioned that “From all the
hospital perspective there are different ways of doing it [triage]”, that is adherence to
established triage guidelines seemed to vary. The same interviewee reflected on the fact that
“When I, as a doctor, get the triage record it can have been half an hour since the
measurements in the record were taken”, showing that discussions on quality are present at
the ED.
Change Management
The change management cub-category was mentioned 10 times through the data analysis of
the interviews. Change management sub-category reflected the reflections on how hard it is to
change the habit of paper based documentation at the ED. Interview #3 showed that “It is both
routine and tradition to perform [documentation] on paper”.
User need for System Support
The final category within the human factors associated with data quality at ED theme is the
user need for system support and represents 61% of the human factors theme. This category
was created by reflections on how the computer systems in use influence and should support
the humans working at the ED. While working at the ED and as described before, two
processes occur at the same time. The patient process flow (the workflow for the staff) and
the documentation process. The two main sub-categories in the system support code are
therefore the workflow and the documentation.
Workflow
The workflow sub-category had 81 occurrences in all 14 interviews. It was described with
four main concepts related to needs or requirements for the computer system to adapt to the
ED staffs workflow: the mobility, the calculation of triage score, the overview and the
checklist and reminders. Mobility is exemplified with an extract of interview #8 “The
technological development at the wards is delayed, so we don’t have tablets or mobile
devices”. Interview #1 mentions “Automatic calculation of the RETTS score? No we don’t
have it. We have to do it ourselves”, and led to the concept of calculation of triage score.
Overview is another concept in the workflow category. Overview of the patient status is
mentioned in interview #11 “We need to see how sick the patient is and if the clinical status is
progressing”. Interview #5 mentions that “One has an overview of all the patients in the
ward, that is not enough with paper for that you need a computer”. This supports the
overview of the ward concept. An overview of the ED process was mentioned by interviewee
#3 that stated “A quick overview of the different parts of the process” was desired. Interview
#9 mentioned that “A system with an overview of the ED allows us to see how much time is
left until I need to measure a blood pressure for that patient”. The checklist and reminders
concept was therefore created in order to represent the need to have reminders or organized
checklists to what needs to be done for that specific patient during the time at the ED.
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Documentation
Regarding the documentation process at the ED, the documentation sub-category was created.
Interview #2 stated that “There should exist a direct connection between maybe the patient´s
id band and the [vital signs] monitor”, and reflected that automatic registration of
measurements are a concept to consider. Logical control of the entered data is another concept
within the user need for system support in documentation. In a statement from interview #8 “
The system does not alert if I enter a 300 value in blood pressure, it should alert in red for me
to be able to see the error”, the need for the logical controls of entered data concept was
supported. Reflections were made on if documented data or patient information was used
retrospectively. A study participant from interview #8 mentioned that “I wonder if we look
that much to older documented values”. Point of care documentation was a concept based on
several statements like this one from interview #11 “Everything is documented in the triage
room computer, just after measurement”. The final concept in the documentation subcategory is the completeness checks in the EHR. A statement from interview #7 shows why
this concept is important “In order to perform a complete triage we have to introduce all the
vital signs included, that is a system requirement to calculate the triage score”.
Theme 2: Technical factors
Technical factors were the second theme that resulted from this study interviews data analysis
code. This theme was related to the influence of technological factors in the quality of data at
the ED. The two main categories in this theme were the interoperability of IT systems and the
standardization of processes and occurred in different percentages throughout the interviews
according to Figure 12.
Factors Associated with data quality at ED.
Technical Factors
Interoperabili
ty of IT
systems (35
ocurrences)
18%
Standardizati
on of Process
(160
ocurrences)
82%
Figure 12. Occurrences of the two categories created and included in the Technical factors theme. Results to all
the 14 study interviews. Interoperability of IT systems represented 19% and standardization of processes 82% of
the Technical factors theme.
The figure below provides an overview of the categories and sub-categories included in the
technical factors theme.
32
Interop
erabilit Standardization of
y
process
Technical factors associated with data quality at ED.
Patient safety standards
14
Standardized documentation
40
Lack of standard
15
Failure to comply
55
Standardization of triage process
72
No interoperability within system
17
Non communicating systems used for…
18
0
10
20
30 40 50
Ocurrences
60
70
80
Figure 13. Occurrences of the categories and sub-categories coded for the technical factors theme. Results for all
interviews. The most common sub-category was the standardizations of triage process, followed by failures to
comply and standardized documentation. Patient safety standards occurred only 14 times through the interviews
data.
Interoperability of IT systems
The interoperability of IT system category in this theme included two main sub-categories:
non communicating systems used for documentation and no interoperability within ED
systems.
Non-Communicating Systems
Lacking of communicating systems used within the ED was a recurrent statement that
occurred 18 times in all interview data. Interviewee #11 mentioned that “We don’t need to use
paper [in documentation], but when we take an electrocardiogram we get a printed copy and
it isn’t available in the patients EHR at once”. This statement shows that printouts from
digital monitors or exam machines are reported to be available on paper during treatment, and
later on scanned in to the EHR. That is the exam and monitoring tools don’t communicate in
an automatic way with the local EHR. The same happens to the settings where a paper based
triage form is used. “We fill in the paper record where we have all the vital signs and all the
medication information. The record is later on scanned” a statement from interview #12.
Non-Interoperability within Systems
The non-interoperability within systems is the second sub-category of interoperability and
includes the presence of “free text” documentation and multiple ways of documenting the
vital signs. According to interview #1 “Yes we dictate and document in free text”. The
multiple ways of documenting vital signs concept, is related to the fact that VS are reported to
be documented in paper, later in the EHR, as free text, in tables or other forms. Interview #5
showed that “We document directly in our paper report form and later on we introduce the
values in the [EHR] tables”.
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Standardization of Processes
The standardization of processes category represents the biggest category within the technical
factors theme with an 82% occurrence in the interviews data. Sub-categories in this section
include: the standardization of the triage process, the failure to comply to and lack of
standards, standardization of the documentation and patient safety standards.
Standardization of Triage Process
Standardization of the triage process is the first sub-category and is the most common in this
category represented by 72 occurrences in the interviews data. A standardized triage process
was reported to be organized and structured according to several concepts. The flow based
triage concept was created from the report that extra actions may be taken depending on the
triage result. That is if after triage “The patient shows changed consciousness level we always
perform a standardized blood sugar test” interview #2. The pre-triage concept represents a
clinical step before triage. The presence of this step would depend on the patient´s emergency
level and whether or not it has already been performed. “Patients arriving at the ED on their
own, may wait in the waiting room until triage”, interview 3, which exemplifies the low level
of acuteness patients where pre-triage may not be present. Ambulance patients represented the
high emergency cases where “Patients arrive via ambulance at the ED and are immediately
transferred to the adequate ED sector” interview #4. Those patients had already been
submitted to pre-triage or triage prior to arrival at the ED. Even if the triage processes were
standardized, some settings reported a variability of this standard according to the time of day.
Study setting #4 participants reported that “We have decided that Monday to Friday between
08:00 am and 21:30 pm, we have a common triage team for surgery and orthopedics”. This
showed that standards used for triage methods at the ED could be adapted to the time of day.
Failure to Comply
Moving on to the next subcategory within the standardization of processes category in the
technological theme, we enter the failure to comply with standards. Failure to comply with
standards was reported to be due carelessness and sloppiness coded by these two extracts
from the interviews: “Not all staff documents re-evaluation measurements of vital signs”,
interview #8 and “[Not documenting re-measurements] may be due to sloppiness or maybe
because we just decide not to do it” #5 interview. High workload coded from the statement
“High patient inflow is often why fail for” in interview #6, is another concept created within
the failure to comply with standards sub-category. However, individual variation mentioned
during interview #5, “It can fell like, if it is a patient that comes for uncomplicated symptoms,
unnecessary to take all vital signs” and the fact that standards are not felt to be important as
described in interview #6 “We only measure breathing rate if that is why they came to the
ED”, are two other concepts included in the reasons or failure to comply to standards subcategory. Even if the main triage processes were standardized in all study settings there was a
reported lack of standard for the measurement and documentation of vital signs upon reevaluation of the patient at the ED. Interviewee #2 stated that “It depends on why we have to
measure the vital signs and that is one of the biggest problems in the ED, to have continuity
and measure continuously”.
Standardized Documentation
The standardized documentation sub-category had an occurrence rate of 40 in all the
interview data and was represented by two main concepts the paper based triage record and
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the template in the EHR. Interview #12 reported that “We write directly on paper and we
follow the vital signs also on paper” showing the use of a paper based standardized
documentation form. Interview 6 showed that templates in the EHR are another way of
standardizing the documentation process “We have the triage template in the EHR where we
document the vital signs”.
Patient Safety Standards
The last sub-category within the standardization of processes was the patient safety standards.
Even if this was the less occurring sub-category, it was important to define it in both check in
check out and identity control concepts. It was reported during interview #2 that “When I
leave or receive the patient at the ED it feels safer if all the vital signs are documented”. This
statement supports the presence of patient safety standards to transfer patients from and to the
ED. Finally, standards were also reported in the patient identification and id control. Also
during interview #2 it was said that “All patients at the ED are registered and provided an id
band on their arm”.
3.2.2 Data analysis and the adopted data quality framework
Several concepts sub-categories, categories and themes resulted from the analysis of this
study interviews data. The presented data will be discussed in detail in the next section of this
thesis, the discussion.
From the results of the data analysis of the interviews, a relation was established between the
themes, categories, sub-categories and concepts of the data analysis code, with those three
dimensions for data quality based on the framework proposed by Weiskopf and Weng in 2013
(23).With this step we related the created code with the chosen data quality framework.
Figure 14 summarises the relation between the data quality framework and the generated
code.
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Figure 14. The relation between the 3 dimensions for data quality framework proposed by Weiskopf and Weng
in 2013 (23) and the code generated from data analysis of the study interviews.
In figure 14, in each code sub-category the three data quality dimensions appear in importance
order. That is the first symbol, after each one of the sub-categories represents the data quality
dimension that is more influenced by that factor at the ED. The two major themes, human and
technical factors, also have a connection to the three main data quality dimensions. Again the
first symbol in the red chain represents the dimension that is more affected by that specific
factor.
All three data quality dimensions were represented in the interviews data analysis results code
and within both of the major themes. All subcategories could be related to all three data
quality dimensions described in the data quality framework used during this study. However,
it was possible to identify the dimension that was more affected by a specific data quality
influencing factor. For example, if clinical competence was deficient the main dimension for
data quality to be affected would be the correctness of the collected data. If staff didn´t know
how to collect vital signs or handle error sources, VS could be wrongly measured and
documented. Furthermore, if clinical competence failed the completeness of the vital signs
data might also be affected, since problems with measurements would result in incomplete
records. The less affected data quality dimension upon deficiency in the staff´s clinical
competence was considered to be currency. This interpretation can be extended to all subcategories of the data analysis code represented in figure 14.
From a general point of view correctness was the more sensible dimension for data quality
that would be easier to affect with both human and technical factors and was followed by
completeness and currency. The way the different categories and sub-categories relate to the
proposed dimensions will be explore further during the next section of this thesis, the
discussion. Figure 15 represents the main founded influencing factors for each one of the data
36
quality dimension.
Figure 15. Most influencing factors of the three data quality dimensions. This figure illustrates which factor has
an higher impact in each one of the three considered data quality dimension at the Swedish ED.
The code obtained from the data analysis of the interviews was also used to complete and
strengthen the previous process description of both patient flow and documentation processes
at the ED. With the visualizations of those two ED processes, it was easier to see exactly
where and when each one of the coded factors influenced data quality at the ED. Themes,
categories and sub-categories were used together with the mapping of the patient and
documentation processes at the ED to relate the staff´s perspectives on which factors affect
vital signs data quality at the ED to the different steps of the ED processes. This connection
related the coded results with correctness completeness and currency, and mapped those
results through the patient flow and documentation processes at the ED.
4. Discussion
This thesis aimed to explore the factors that influence the quality of data collected and
documented at the ED. Different ED settings were studied in order to gather information to
map patient flow and documentation processes within the ED. The code that resulted from the
analysis of the interviews was both compared to a proposed framework from data quality and
used to better understand and map the different factors affecting that affect vital signs data
quality in the ED.
A general description of the main findings of this thesis will be presented, followed by a
reflection on the study methodology. The methodology reflection of this study will consider
limitations and strengths, trustworthiness, credibility and dependability and will conclude this
section.
37
4.1 Patient flow, documentation processes and staffs insights on data quality
in the Swedish ED
This thesis showed that patient flow and documentation processes take place at the same time
at the ED. Quality of vital signs data documented in the ED can be affected by both human
and technical factors that have an impact in the data completeness, correctness and currency
in the patients EHR. This study showed that human factors represented 58% of the factors that
could have an impact in data quality at the ED suggesting that, even if technical factors exist,
human factors seem to be the most influencing ones.
At the ED both patient flow and documentation processes need to happen without friction and
are therefore dependent on a pre-defined structure in order to work. Constant evaluation and
improvements of those ED processes allow staff to work with those processes in a
constructive efficient way. This generates a cycle of constant structured based work that is
often evaluated and improved in order to support ED staff in their work. This cycle was
named the ED cycle and is represented in figure 16 together with the major themes, categories
and data quality dimensions studied.
Figure 16. The ED cycle. This cycle results from both patient flow and documentation processes at the ED.
In figure 16, the human factors that affect data quality at the ED are represented on the right
side, in orange together with their major categories. The technical factors theme and
categories are represented on the left side of the figure, in blue. The three data quality
dimensions, proposed by the framework used for this study, are represented at the center of
38
the cycle.
4.1.1 Standardization of Processes
Both patient flow and documentation processes in the ED need to be structured. The patient
flow process is connected to several clinical and standardized steps that allow the patient visit
at the ED, to follow a similar pattern independently of the considered hospital setting.
Standardized procedures
Standardized triage procedures were present at the ED of all studied clinical settings. Triage
structure and standards were pointed out during the interviews, as one of the factors that may
affect the quality of data in the patient records within the ED. This factor was coded as the
standardization of triage process sub-category within the technical factors theme. The
documentation process follows side by side with the patient flow process, and represents the
moments within the patients visit to the ED where documentation of patient related
information, such as vital signs, is performed in to the patient record. Triage documentation
was described in the settings to happen in one of three ways: on paper, digital or both.
Regardless of the way it was performed documentation was structured, which led to the
standardized documentation sub-category. Interview data analysis results indicate that the
presence of standards for triage procedures and documentation are needed, in order to ensure
that ED visits are structured and organized. A structured triage procedure seems to ensure that
all patients are received and treated in a similar way. Furthermore, triage process
standardization seems to contribute for the completeness, correctness and currency of the
information documented in the patient record. When using a standard to perform triage and
when structure for triage variations (such as the need for extra exams in special cases, the time
of day or the way patient s arrive at the ED) is present, staff is given the opportunity to collect
enough information that ensures that the patient record is complete.
Compliance to Standards
The patient flow process suggested, however, that triage is performed in full scope for all
patients arriving at the ED, but orthopedics patients were often reported to not be submitted to
a full triage indicating that, even if standards and structure are available, there are failures in
standards compliance. Data analysis findings prove that there is lack of compliance with the
standardized process of triage due to, either patient individual variation or the fact that
standards are not felt to be relevant. Even high workload and carelessness were reported as
reasons not to comply with triage standards. This implies that the completeness of the triage
information recorded or documented in the patient record may be affected by the same
reasons that lead staff to fail to comply with standards.
How to improve standards
Documentation of triage information in a structured paper form or in an EHR template can
help to improve completeness of the record, by allowing staff to have a form or guide for
documentation. This specific point is related to the impact and the needs of a supporting EHR
system that provides staff with checklists and process overviews, and that will be discussed
later on. By providing an overview of the triage process and the patient information, failure
to comply can be minimized as staff is reminded of all the information that needs to be
39
collected. Furthermore, if standards are extended to all clinical steps within the patient flow
process even completeness of the information of the patient´s re-measurements, for example
vital signs, after triage can be improved since results show that standards for both
measurement procedures and documentation of these repeated measurements is lacking.
But even if standards may help to achieve completeness in the EHR, the way standards are
supported and delivered seems to have a big impact on both correctness and currency. If
triage documentation is performed on a paper-based standardized form, it is reported that, for
example, vital signs information is documented immediately after measurement with no need
for intermediate information support, and therefore minimizing the risk for errors. Because
information is written exactly after measurement timeliness is extremely accurate. Correctness
and currency seems therefore to be optimal with paper based documentation. Digital based
settings reported that computer stations were available physically “at point of care”. This
means that when information was collected it was immediately timed and documented in to
the EHR. This point of care documentation had been achieved by several organizational
strategies such as, having a triage team consisting of two RN where one performs the patient
contact and measurements and the other documents collected information directly in to the
patient record.
Digital Settings
Digital based settings documented in a standard EHR template that allowed the user to move
further without introducing all the information in the template. For example, if vital signs
were measured, the user could introduce only blood pressure and temperature and be able to
move forward in the system. This possibility represents a risk for the completeness of the
patient records. Since there was no obligation to introduce information on all the vital signs,
there is a risk for the patient record to be incomplete. This was common in the different EHRs
available at the settings. Furthermore, systems did not provided logical controls of introduced
data, another concept that will be described further later on, affecting therefore the correctness
of the documented information. Currency seemed to be present in digital based settings since,
as mentioned before, information was introduced in to EHR directly after collection. When
triage documentation was performed with both paper and digital support, data quality
dimensions seemed to be highly affected. These settings described that triage data could either
be only collected or collected and documented in a paper and later on introduced in to the
EHR. This means that using the vital signs example once more, if measurements were
performed either staff would document it in a piece of paper or just don’t document at all until
available time and computer allows the introduction of the information in to the patient
record. This primary paper documentation implies that correctness, completeness and
currency are affected. Lack of standards during the collection and primary documentation of
the patient data leads to higher risk for incomplete gathering of information affecting
completeness. The fact that information from several patients might be primary documented
in this paper increases the risk for information to be incorrectly introduced in to the patient
record. Finally, because data is only introduced in to the electronic patient record later on and
after the measurement, the user needs to set back the clock to the time measurements were
performed, which normally is forgotten resulting in a lack of accuracy in the data currency.
Standards and Data Quality
Standards are the base and building blocks for achieving better structures and routines that
allow patient flow and documentation process in the ED to work. Standardization of processes
40
was the strongest category included in the technical factors theme confirming that this is a
major factor associated with data quality at the ED. This finding is consistent with “the fact
that structured entry, compared to handwriting, may encourage recording of specific or
otherwise unincorporated data elements resulting in a more detailed record”(44). Data quality
can be affected in its completeness, correctness and currency if standards fail to exist, work
properly or even if staff fails to comply. Patient safety, specifically during ED treatment and
upon discharge to home or other ward, is highly dependent on the existence of standardized
process that help documented patient data to be complete, correct and current so it can
trustfully be used.
4.1.2 Interoperability of IT systems, Education and Competence
When standards exist they need to be tested and used in the next ED cycle phase, the working
phase. In order to work, standards need both technical interoperability of IT systems and
human education and competence. The patient flow and documentation processes show that
patient record information is collected and used from the patient´s arrival at the ED, all the
way until after discharge.
Human requirements
Both processes are dependent on the education and competence of staff members that ensure
that the processes happen in a structured, organized and quality way. To be able to use,
collect and know how to find patient related information through the ED, education and
competence is required from the staff. Competence and knowledge on both clinical and
methodological procedures is extremely important in order to ensure correctness of the
collected data. During triage, vital signs measurements are dependent on several tools that
need to be known by the staff in order to provide accurate measurements. But sometimes
plausibility of the obtained values needs to be accessed in order to see, whether that
measurement is plausible for that particular patient or not. Plausibility appeared to be related
to clinical experience. It was implied that the correctness of the documented patient data in
the EHR could be influenced by clinical, methodology competence and experience. Senior
staff members were reported to easily react to an error suspicious measurement. Several
settings were aware of this factor and performed, therefore, several controls on staff´s
competence. Measures such as educational sessions on new measurement tools and
spontaneous control of ED patient records seemed to be used in order to detect failures and
improvements areas. But even if education was provided and competence tested, sloppiness
and carelessness was reported, showing that even if standards and structures are present,
several factors may influence humans working at the ED and therefore affect the quality of
data in the patient records. All three data quality dimensions correctness, completeness and
currency can be affected by these attitudes since effects of sloppiness and carelessness can go
from wrong values documented in wrong patient record, incomplete introduction of data and
even no attention to the time when measurements were taken. As mentioned before patient
flow and documentation processes show that information moves from patient arrival to after
ED discharge.
Technical requirements
In order to allow this information to be available and to work in the ED, technical
interoperability of IT systems is required. When paper based documentation procedures are
41
present in triage it is important to consider that patient information recorded in those forms is
only accessible physically at the ED. The use of information in a distance form (for example
if other wards need to be consulted) is not possible since there is no interoperability between
the paper form and the patients EHR. The same problem occurs when ambulance services
provide a print out of their journals or when specific exams, such as electrocardiograms, are
required. Information contained in those papers is reported to be scanned after the patients
visit to the ED. Digital settings may also have a delay in introducing patient related
information, as mentioned before, when they use a paper based primary documentation. The
fact that ED data is not available at once in the patients record, affects the completeness of the
record, since information of actual or recent treatments may be missing. Even if paper form
for documentation were considered to be optimal in their completeness, correctness and
currency related to their standardized form and use, they fail grandly from a communication
or interoperability of systems used for documentation point of view. But even digital based
documentation settings may see completeness and correctness of ED EHR data affected when
free-text based documentation is allowed or when there are multiple ways of introducing the
same information. Using again the vital signs example, EHR systems may be organized with
different sections where free text documentation is possible when tables or templates are not
provided. Information on vital signs measurements may be therefore documented in different
sections depending on the staff. This fact may affect completeness since not all vital signs
might be documented and correctness since they even might be recorded in the wrong
sections of the EHR.
ED Work Support is Essential
When standards exist and, are well known and used with the correct support they contribute to
working patient flow and documentation processes. Education and competence together with
interoperability are the perfect example of how human and technical factors interact and may
affect the quality of data in the ED. Human factors such as the lack of physicians with
advanced experience “stresses the need for solutions that simplify documentation and allow
more time for direct patient care” (45). It is therefore essential to develop solutions that can
create a balance between the human needs and the technical solutions. A way to develop these
solutions is to gather information from the clinical sites. That is why constant evaluation and
monitoring of ED policies and work is needed.
4.1.3 Management
Management strategies are of importance throughout the patient flow and documentation
processes. This is a major part of the ED cycle where standards working on a particular
setting are evaluated, controlled and eventually changes in order to improve the quality of
care and consequently the quality of EHR data in the ED.
Management and Quality Control
Interview results revealed that the described patient flow and documentation processes are a
result of management strategies, associating this category to 18% of the human factors
influencing quality of data in the ED. Management includes both quality control and change
management and is a category that can have impact in all three data quality dimensions.
Quality control by follow up on data quality, adherence to standards or guidelines and even
discussions on quality has the power to raise the data quality subject within the ED working
setting. Both follow up on the quality of data recorded in to the ED EHR, by retrospective
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controls or the follow up on the adherence of the established standards for triage and ED, such
an evaluation of the completeness of the vital signs in ED patient records, provide valuable
information about the actual status of data quality in that specific setting. To discuss data
quality with staff was often referred during the interviews as one of the measurements that
would help improve the quality of data recorded in to the EHR. Interviewees reported that
they feel more engaged to perform changes if they know why changes are needed. Taking the
vital signs example again if discussion regarding the importance of vital signs documentation
in the patient records is raised, staff may feel more engaged to perform change and reduce
factors previously mentioned such as sloppiness and carelessness. It is assumed therefore that
if ED staff was provided with knowledge on the factors that define quality of data in the
patient records, they might help to improve correctness, completeness and currency. Settings
that used a paper based documentation process named that habits may be hard to change,
paper forms seems to be a part of their cultural ED setting and therefore considered almost as
a tradition. Both evaluation and change are needed to improve data quality in the ED, and
both are dependent on management and quality control strategies. Finally this section support
previous studies on how important motorization and management of change is for the
improvement and development of EDs (45)(26). Rao et al wrote that: “Implementers should
carefully consider how implementations will affect information flow and then expect the
unexpected”(46).
4.1.4 User need for System Support
The final step within the ED cycle is the one that takes place when standards are present, used
and evaluated and new support is developed to help staff with the execution of both process
flow and documentation processes. These processes show that documentation in the ED is
tightly related to the different steps in the patient flow process.
Staff Workflow Support
Factors related to the way EHR systems support staffs work in the ED, were coded system
support and represented the biggest part (61%) of the considered human factors that affect
data quality in the ED. As mentioned before, the patient flow process can be related to the ED
staff workflow, and an EHR system needs to adapt to that workflow in order to allow staff to
perform better. During the study interviews factors such as mobility and calculation of triage
scale were mentioned as possible sources for improvement of data quality in the ED. Paper
based documentation settings showed us that the bedside documentation provided a high level
of data correctness, completeness and currency. It is therefore proven that in order to allow
improvement of the quality of data in the ED, a system needs to be portable and mobile to
follow both staff and patient through the ED process. This finding is supported by previous
study that concluded mobile devices “improved patient physician interaction, improved
workflow and structural iPad benefits promoted their demand. Physicians perceived the
structural benefits of iPads would improve patient physician interaction and improve
workflow in the ED”(46).
Support and Data Quality
Data quality correctness may even be further improved if the system provides a tool that
automatically calculates total triage scores. If the system allows an automatic calculation of
the total triage score for a patient after the introduction of all the contents and information
collected during triage, correctness may improve since calculations errors are less likely to
43
occur. However, if the wrong values are introduced in the wrong EHR, or if incomplete or
wrongly measured values are used, correctness will instead decrease, as mentioned before
during the structure and standards section of the ED cycle. As discussed also in that section
the use of checklists and reminders may also have a positive impact in the data correctness,
completeness and currency. If checklists are used the risk of forgetting a task, measurements
or introduction of needed information seems to be less. These solutions may also have time
planed tasks that allow staff to plan their actions and improve currency by allowing timeliness
in the EHR data to be current. Patient flow process showed us an overview of the patient´s
process through the ED. In a similar way staff mentioned that they need to have a constant
overview of everything related to that patient during that ED visit. Again this is easily
achieved by the paper based form where it was mentioned that one can easily go through the
patient´s information in a quick and direct way. Systems supporting staffs workflow in the ED
need therefore to be able to provide overviews on the patient and their process in the ED.
Some settings also described that their current system allows also an overview of all ED
providing also the support to communicate via instant message via computer with other staff
members. The fact that an overview of the ED was provided seemed to be a great feature
because participants appreciated the fact that they could see staffs availability and from there
regulate and plan their actions in a more structured way. Structured support adapted to the
staff´s workflow was suggested to help improve the quality of data but support on for the
documentation process represented the majority of the occurrences within the system support
category suggesting that this may be the support that could have greater impact in the quality
of data in the ED. All three considered data quality dimensions were suggested to be able to
be improved when documentation support was present in the ED system. Monitors for vital
function are used in some of the studied settings and were referred in some interviews as a
way to improve correctness of, for example the vital signs documentation. It was stated that if
monitors were automatically connected the ED EHR, they would allow values to be
introduced in the patient record in automatic way without the staffs intervention helping
therefore to improve correctness, currency and even completeness. However, discussions
from automatic registration of measurements form monitors mentioned a previous described
factor, patient security. Some interviewed RN mentioned the fact that patient identity would
need to be controlled in order to be able to use this direct documentation from monitors in to
the EHR. There is an obvious knowledge of the fact that the use of direct documentation from
monitor to the patient record may either improve the studied data quality dimensions or
damage them, depending on both human and technical factors.
Improvement of Recorded Data by System Support
Mentioned before during the description of the working part of the ED cycle, was the need for
systems to have a logical control of entered data in order to avoid errors and improve
correctness. An example is the fact that actual EHRs, allow the introduction of systolic blood
pressure measurements of 300 millimeters of mercury, a value impossible to obtain. Also
completeness checks, mentioned before improve both the staff´s compliance to standards and
the completeness of the data documented in to the patient record. As mentioned before when
documentation occurs directly after the execution of the measurements or clinical treatments,
all three data quality dimensions seem to be improved. That is why a point of care
documentation should be provided in order to adapt the system to the ED documentation
process needs. Finally it was previously described that settings that use a paper based
documentation process, lacked the opportunity to both allow ED patient information to be
available to other professionals, outside the ED and to consult previous patient related
information stored in the patient record. The retrospective use of the data should therefore be
44
available in order to allow documentation performed during the patients visit at the ED to be
available in a complete, correct and current way.
This final step in the designed ED cycle continues further on with a new structure and
standards phase, that will be again tested and evaluated leading to new improvements and
changes that will support the ED processes. Even if the presented ED cycle structured this
process in separate phases and with distinct influencing factors for data quality, it is important
to have in mind that both human and technical factors can be related to all cycle phases. The
ED seems to need to have a sensible balance between both human and technical factors that
support and ensure both patient flow and documentation processes, in order to achieve EHR
data quality. Completeness correctness and currency don’t seem to be able to achieve only by
making changes or improving either human or technical influencing factors. An adequate
balance between them seems to be the key to success.
4.2 Methodology considerations
Qualitative methods were used in this thesis to gather information and knowledge on the
study subject. The sampling strategy described was used in order to try to select participants
with a deep or good understanding in both ED patient flow and documentation processes.
Multiple settings were considered in this study which allowed different perspectives and
strategies to be known and studied. The use of a known and previously described framework,
allowed the researcher to search for factors influencing the quality of data in Swedish ED.
Interviews were a powerful tool to collect insights and perceptions from the ED staff. The
structure used during the first interview, the pilot interview, was optimized and strengthen in
order to focus on completeness, correctness and currency of the ED data. At the end
saturation was present since no new information was gathered and the few last interviews
helped reinforce collected data from the previous ones.
In the following sections issues such as study limitations and strengths, trustworthiness,
credibility and dependability will be discussed.
4.2.1 Limitations and strengths of the study
Even if recognised qualitative research methods were used in this study the researcher knows
that the validity of the findings may be affected by limitations.
The first limitation of this study is the language. The fact that all interviews and part of the
literature research was performed in Swedish, means that collected data needed to be
translated to English prior to data analysis. This step may represent a risk to misinterpretation
or wrong translation.
The initial use of open-ended questions during the pilot interview allowed the interviewee to
reflect and describe both triage and documentations methodologies in depth. But the
information collected during this interview might have been insufficient regarding the three
main factors for data analysis used during this study: correctness, completeness and currency.
Another limitation is the fact that an extended relationship was not established with the
interviewee since they were only interviewed once. Multiple interviews or group interviews
may have been ideal. However, by performing small group and mainly individual interviews
45
data was collected and analysed in a more in depth way which strengths the conclusions
gathered to each one of the settings.
Since the inclusion criteria used in this study required or asked for participants to work as RN,
because they are the ones responsible for triage in Sweden, a limitation may be the fact that
the pilot interview was performed with a medical doctor and not a RN. A new and improved
questionnaire was developed after this pilot interview and again tested once more with the
first RN interview, which ensures that the achieved and selected questions were adapted to the
information that needed to be collected even with RN participants.
Limitations might also be considered in the process results. A general patient flow and
documentation process for the ED was created by the researcher from information collected
during the interviews and the analysis of the settings ED documentation. These generalised
processes might not be able to be true in all ED settings, such as smaller hospitals or ED
outside Sweden, since they were developed based on information from each one of the study
settings. But the fact that eight different main hospitals were studied might also be seen as one
of the study strengths for the use of the generalised processes in other main Swedish ED.
Finally, I believe it is important to consider that data was collected from healthcare
professionals and that may represent a bias in this study when considering the fact that human
factors influencing data quality at the ED occurs more often than the technical ones. This
might be explained by the fact that for healthcare professionals human factors are more
natural than technical ones which may have influenced the fact that human factors were more
recurrent in the data collected in the study.
4.2.2 Trustworthiness, credibility and dependability
Trustworthiness in this study was ensured by peer debriefing of the study process from the
researcher team. As recommended, “the team sough feedback on the evolving findings and
interpretation of the data”(38) and the detail in coding and analysis was consolidated by
saturation.
A strategy that was used to validate this study is a primary qualitative researcher´s strategy
called triangulation. According to Creswell, 2008 triangulation is the process of gathering
evidence from different individuals, types of data or methods of data collection in descriptions
and themes in qualitative research. This thesis used triangulation of analysed data that is,
evidence from the main supervisor and clinical advisor, data collected from interviews and
even documents related to the triage and ED from the different clinical settings contributed to
the data analyses process. The researcher examined each one of the information sources and
founded evidence to support the created themes providing this report with both accuracy and
credibility.
Finally, dependability is an evaluation criteria used when a phenomena may change
depending on the environment, research method or time resulting in instability to the research
(39). Even if findings differed from setting to setting the influencing factors for data quality at
the ED described, should be able to be found in similar settings by similar methods.
4.3 Implications of this study : Recalling the sepsis example
Going back to the clinical example of sepsis patients, presented during the introduction part of
this thesis, is a way of showing how the finding soft hi study can improve sepsis diagnosis
and treatment. As mentioned before sepsis diagnose needs to happen as soon as possible upon
46
vital signs changes in order to improve patient chances to survive the disease. It was
mentioned that sepsis patients missed vital signs documentation in their journals, which can
mean that either vital signs were not fully measured or they were but documentation was not
performed.
Using this Sepsis example, the way to improve sepsis patient´s diagnosis and outcomes would
be to create a clinical decision support that assists ED staff. Benefits of the creation of such a
system would be the use of a standardized and organised way for documenting patient related
data with the contribution for the improvement of the data quality, and the instant use of that
data to assist clinical decisions and treatment at the point of care. If all vital signs data needed
to be introduced in the CDS, in order to move forward within the system, completeness would
be ensured. Furthermore, eventual direct link between vital signs monitoring stations and the
patient EHRs would allow an instant feed of vital signs data ensuring that they are currently
documented even improving correctness, since VS would be automatically measured.
Alternatively, the use of bedside stations or mobile devices for ED documentation would also
contribute to improve vital signs currency at the EHRs, since the measurements would be
performed and documentation possible directly afterwards. Staffs education and sensibility
for data quality issues would also contribute to reduce the incompleteness and incorrectness
level of today´s sepsis patient´s EHRs.
So a way to easy and improve documentation of vital signs upon triage and early diagnose of
sepsis patients would be a clinical decision support system. The system would provide
healthcare professionals warnings and treatment recommendations at point of care that
eventually would have an impact in sepsis patient´s clinical outcome. A practical example is
that the CDS would warn if not all vital signs were introduced in the patient record, and if the
breathing rate was altered, warnings could be activated in order to alert for eventual sepsis
diagnose. But the design, development and functionality of an accurate sepsis CDSS needs
introduction of quality data. This is when understanding and knowing the factors that can
affect data quality at the ED is essential. The findings of this study therefore a first step to
identify and recognize potential influential factors of data quality in the ED.
4.4 Future research
Future research may evaluate how currency can be improved on vital signs data. In this effort
researchers may use again the Sepsis example to quantify data and see how currency is
affected in a more detailed way by using quantitative study methods. This research may lead
to the design and development of a CDS for early detection of, for example, Sepsis patients.
Finally, the impact on the healthcare professional’s performance and workflow as well as the
patient outcomes and quality of vital signs data recorded at the ED EHRs, call for even
additional studies and evaluation aimed for the development and continuous improvement of
this subject.
47
5. Conclusion
This thesis concludes that Swedish EDs contain barriers for achievement of EHRs data
quality. The mapping of both patient flow and documentation processes performed in this
thesis, together with the described factors that can influence those processes and impact data
quality in ED EHRs, provided an overview of both the barriers and the opportunities to
achieve quality of data in the ED patient records. Human factors were the ones pointed to
have a higher impact in the quality of data at the ED. However, the major impact in
completeness, correctness and currency of data at the ED is achieved when both human and
technical factors are considered. There seems to be an essential need for supporting the way
documentation is performed at the ED in order to improve data quality. This study concludes
that a way of ensuring that electronic systes work reliably on the data from patients EHRs, is
to introduce standardized documentation. The use of standards is already in place for the
clinical and patient related procedures, but seems to still be under development for the
documentation routines. This study identified the major factors that can affect data quality at
the ED and the findings should be extended in the future to help understand better those
factors and the way to improve them.
48
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Appendix A- Main interview Guide (In Swedish).
Intervjuunderlag datafångst sepsisstudie, dokumentation akutprocess.
Intervju med:
Arbetar på, sjukhus, enhet:
Erfarenhet från akutsjukvård:
Insyn i rutiner kring akutflöde medicin / kirurgi / ortopedi / annat?
Klinisk tjänstegrad:
Akutprocess mätrutiner:
Beskriv hur er triageringsprocess ser ut.
Beskriv vad som påverkar om om patienter triageras fullt ut eller inte?
Är icke triagerade patienter friskare än triagerade?
Akutprocess dokumentationrutiner
Beskriv hur ni dokumenterar triagering och vitalparameterar
Beskriv Hur kompletta du uppfattar att registeringarna av vitalparametrar är
Vilka faktorer ser du dom stärker kompletthet?
Vilka faktorer uppfattar du som hinder för kompletthet?
Beskriv Hur rätt du uppfattar att registreringarna av vitalparametrar är
Vilka faktorer ser du som stärker korrekthet?
Vilka faktorer hindrar korrekthet?
Beskriv Hur rätt tidsstämplarna kring vitalparametrar är?
Vilka faktorer stärker tidsfaktorn i mätning och registrering?
Vilka faktorer hindrar tidsfaktorn i mätning och registrering?
Om pappersdokumentation:
Vad hindrar er från att gå över till digitala arbetssätt?
Appendix B- Patient flow and documentation process for Hospital settings
A; C and D.
Hospital Setting A
In this setting patients arriving alone at the ED are registered by a primary team that forwards
patients to the correct ED. Vital signs may not be measured on all patients in this step. If patients
show minor emergency care need they will be forward to the minor emergency department
“lättakuten”, if not patients will wait in the ED waiting room for their turn. While waiting if their
clinical status deteriorates they will enter a fast line called “larm”, the same used by ambulance
arriving patients and where patients meet the emergency team at once. Patients arriving by
ambulance have their clinical information, including vital signs, transferred verbally to the ED team
or digitally send to their EHR. This digital transfer is however not instant and might take several days.
From this point and when stable patients are forward to one of the ED wards where vital signs remeasurements may happen. These re-measurements are expected to be documented at once in to the
EHRs, but are reported to not always be documented. Patients that have been waiting in the ED
waiting room meet a ED team that executed triage and forward the patient to one of the ED wards.
This team is expected to perform documentation directly in to the EHRs, but can reportedly perform
documentation first in a paper and later in the EHRs, when high patient intake happens.
53
Hospital Setting C
In this setting patients arriving alone at the ED take a queuing number and wait for their turn. When
their turn comes a primary team performs an outer triage that forwards patients to the correct ED.
Vital signs may not be measured on all patients in this step. If patients show minor emergency care
need they will be forward to the minor emergency department “lättakuten”, if not patients will be
forward to another desk and later on to the respective ED ward. If their clinical status deteriorates
they will enter a fast line called “larm”, the same used by ambulance arriving patients and where
patients meet the emergency team at once. Patients arriving by ambulance have their clinical
information, including vital signs, transferred verbally to the ED team or as a monitor print out on
paper that is delivered to the ED team upon arrival. Information received verbally is written on paper
and later on documented in to the patients EHR. All vital signs measurements and documentation at
the different ED wards is performed directly in to the patients EHRs without paper support.
54
Hospital Setting D
In this setting patients arriving alone at the ED take a queuing number and wait for their turn. When
their turn comes a primary team performs an outer triage that forwards patients to the correct ED
ward. During night time this outer triage is not available and patients are forward directly to the ED
wards. Vital signs may not be measured on all patients during outer triage. If patient´s clinical status
deteriorates they will enter a fast line called “larm”, the same used by ambulance arriving patients
and where patients meet the emergency team at once. Patients arriving by ambulance have their
clinical information, including vital signs, transferred verbally to the ED team or as a monitor print
out on paper that is delivered to the ED team upon arrival. Information received verbally is written on
paper and later on documented in to the patients EHR. All vital signs measurements and
documentation at the different ED wards is performed in to a specific ED paper form that is later on
scanned in to the digital patient record. This scanning process is reported to take several days.
55
Appendix C- Mind map of data analysis code results. Themes, categories, subcategories and concepts obtained through analysis of this study interviews
56