Survey on Wearable Medical Devices Data
in Health Information System
Done by:
Raghad abo dalhoum (0215591)
Yasmeen almohtaseb (0216327)
Survey on Wearable Medical Devices Data in Health
Information System
ABSTRACT: Healthcare data might not always be for medical
records, they could also include data in activities and operations
related to healthcare organizations. The integration between systems
in healthcare and other related operations systems is meant to
improve the quality of healthcare service, and improve the patients’
outcomes. interoperability of these data within several institutions is
an important issue as in healthcare for individuals and communities
(HIMSS), so it is evident that there‘s necessity to create a dynamic
technical environment for the medical informatics professional to
help them in the knowledge acquisition and support their learning
process.
Figures
Figure 1: Types of Health Wearable Technologies (HWT). ________________________
Figure 2: The Levels (HIS) Work Over. ________________________________________
Figure 3: Data Exchange Protocol. __________________________________________
Figure 4: Growth in use of Wearable Health Technology in healthcare systems. ______
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4
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Contents
ABSTRACT ................................................................................................................................... 1
1.
Introduction ........................................................................................................................... 3
2.
GOAL & OBJECTIVES ....................................................................................................... 7
2.1 Goals ...................................................................................................................................... 7
2.2 Objectives .............................................................................................................................. 7
3.0 Statistical Analysis ................................................................................................................... 8
4.0 Conclusions ............................................................................................................................ 10
References .................................................................................................................................... 10
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1. Introduction
With the wide spread of various data sources in the medical field, several issues arise
related to combining and integrating these data to make them easily accessible and
manageable, to be used in different applications, like education.
The health information management systems society (HIMSS) defined data
interoperability within health information systems as: having the ability of various data
sources in information systems and connected devices, to “access, exchange, integrate and
cooperatively use data”, with coordination between different health organizations that exist
within the same region or outside the region, aiming at providing health information on
time, and with seamless portability, in order to enhance the quality of health services of
individuals and globally [HIMSS.org].
These health wearable technologies (HWT) can be attached to clothes, or adhesive to skin,
or as accessories (like a wrest watch or a pair of glasses). They collect data and send them
to a remote location (like a clinic or a hospital) for record keeping or for taking medical
action (see figure 1).
Figure 1: Types of Health Wearable Technologies (HWT).
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Interoperability between health information systems (HIS) within different organizations
was set to work over three levels mainly, but then another level was later added to cover
all aspects of an interoperable HIS [NCVHS report, 2000] (see figure 2). These levels are:
1. Foundation level: where an inter-connectivity environment setup is defined, to ensure
the secured data exchange between systems.
2. Structure (syntactic) level: where the format, context, style, syntax of the data to be
used with connected organizations are defined, as a preparation for interoperation.
3. Semantic level: where the underlying models to distribute and use data are set, to be
able to build a standardization common ground for coding vocabulary, to ensure the
understanding of terms.
4. Organizational level: this is involved with the external assets to organizations, like
politics, social aspects, legislations, and organizational structure that facilitate and ensure
the security and maintainability of the exchanged data. The defined components in this
level enable shared consent, trust and integrated end-user processes and workflows.
Organizational
Semantic
Structure
Foundation Level
Figure 2: The Levels (HIS) Work Over.
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The International Standardization Organization (ISO) defined a structure of 7 layers of data
exchange protocol (see Figure 3), these include: application, presentation, session,
transport, network, data link, and physical.
7. Application
6. Presentation
5. Session
4. Transport
3. Network
2. Data Link
1. Physical
Figure 3: Data Exchange Protocol.
The Open Systems Interconnection (OSI) protocol specified the level 7 (application) with
protocol to deal with health data, and called this protocol and the standards that it defined
the “HL7” standards, that were approved and published in November, 2000 [Benson,
2012].
Workers in HIS find data associated with this discipline complex, since it involves diverse,
interdependent, and knowledge-intensive and dynamic information that is continuously
changed and enlarged [Amalberti and Vincent, 2016].
More recently, usage rates have been reported at above 90%, but dissatisfaction with the
impact of healthcare information systems on workflow and patient throughput remains high
[Peckham, 2016].
Usage of wearable health technology show that 71% of physicians who adopted this
technology used them for e-prescription, and 38% of them admitted that they made records
exchange with different health care related organizations easier [Medical Economics,
2018].
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The following chart shows the growth in use of wearable health technology in healthcare
systems over the past 6 years (see Figure 4) [From: e-marketer statistics, 2019].
growth in use of wearable healthcare technology
100
number of users (thousand)
90
80
70
60
50
40
30
20
10
0
2014
2015
2016
2017
2018
years
Figure 4: Growth in use of Wearable Health Technology in healthcare systems.
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2019
2. GOAL & OBJECTIVES
2.1 Goals
For almost every system, the challenge of integrating data from various sources is a
challenge. So, for health information systems, the challenge stands. This puts a goal to this
integration to aggregate these various data that comes from different sources, in different
formats, unified and compliant with the standards of the health information system that
uses them, and to enable the system to present and analyze these data easily and make them
understandable, and then present them in a user friendly manner.
The major goal of this research project is to develop and test an integrated multi-source
digital health information system (HIS) for teaching students about digital health, medical
and health informatics, to support the process of knowledge acquisition and building digital
health skills and competences. As without it, students will not have any way to practically
understand what they learn in theory, and the projects also aims to cut down required
training period for graduates by allowing them to deal with integrated systems practically
before graduating, and thus they will have a good idea about how these systems do operate
inside hospital in real life.
Teachers’ roles in such teaching environment needs to be as a guide and a monitor on
students’ activities and interaction with the received data. Innovative educators have found
the integration and usage of health wearable devices in teaching a rich area of research,
and developing curricula that adopts the use of these technologies a must have discipline.
This shows that there is a critical need to develop reliable approaches to integrate data that
come from different sources, in a standardized and uniform manner. When adopting the
data and the devices in healthcare education, certain aspects need to be taken into
consideration, and some alterations need to take effect on the course curriculum and the
teaching methodologies to support the integration of data related to the medical field to be
used in different applications, like education.
2.2 Objectives
The main objective behind developing this research project is to produce and test an
integrated, multi-source digital health information system (IHIS) for teaching students of
digital health parameters, medical and health informatics, in order to support the process
of knowledge acquisition and building digital health data analysis skills and competences.
In addition to the aforementioned objective, this research projects will also:
1. Conduct an analysis of existing policies and practices in digital data integration and
benchmarking of existing IHIS solutions and approaches;
2. Identify the requirements (technical, operational, legal, and ….) for integrating disparate
digital health systems (devices and/or software) into a single digital information
environment;
3. Develop a prototype of an Integrated Health Information System for study programs
Medical and Health Informatics at European Campus Rottal-Inn of Deggendorf Institute
of Technology;
4. Evaluate the prototype
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5. Define specific learning and training scenarios
6. Evaluate feasibility of the developed learning and training scenarios among cohorts of
students of medical and health informatics
7. Suggest a set of recommendations on developing Integrated Health Information Systems
for educational and research purposes.
8. Use cases of artificial intelligence, computer vision, and machine learning in prototype
development like collect real-world data and evidence
3.0 Statistical Analysis
Data were expressed as mean (standard deviation). The Dunnett test, for which standard
criteria were set as references, was used for comparing variables estimated by wearable
devices during the use of the metabolic chamber method and the DLW method. The mean
absolute percent errors (MAPEs) relative to the PAEE values estimated using standard
methods were calculated to provide an indicator of the overall measurement error. The
Pearson and Spearman correlation coefficients were used to examine the relationship
between standard criteria and variables estimated by wearable devices. Modified BlandAltman plots [Krouwer, 2008] were used to test proportional biases between standard
methods and devices, and the correlation coefficient of the standard criteria and the
differences between the standard criteria and each device were examined for significance.
Accuracy for different devices was calculated using Equation (1) (see equation 1):
𝐴𝑐𝑐 =
∑ 𝑀𝑒𝑎𝑠𝑢𝑟𝑒𝑑 𝐶𝑂2 𝑙𝑒𝑣𝑒𝑙
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 ℎ𝑜𝑢𝑟𝑠 𝑤𝑜𝑟𝑘𝑜𝑢𝑡
Equation 1
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……………………….. (1)
During all analyses, P<.05 was considered statistically significant. All statistical analyses
were performed with SPSS version 20.0 for Windows (IBM SPSS Japan Inc, Tokyo,
Japan). Table 1 below shows a comparison between different wearable devices, and the
measure metabolic rates (in kilo calorie per day – kcal/day).
Table 1: The measure metabolic rates for different wearable devices.
Devices
Placement Basal metabolic 15 free-living days
rates (kcal/day),
Invalid
Non-wearing time in valid
average
days
day
min/day,
average
kcal/day,
average
Fitbit Flex
wrist
1360.4 (195.2)
1
42.4
(18.4)
26.9 (23.4)
JAWBONE
UP24
wrist
1312.6 (157.1)
0
40.1
(13.0)
25.4 (22.9)
Misfit Shined wrist
1708.0 (245.9)
15
40.4
(13.2)
26.1 (23.1)
EPSON
wrist
d
PULSENSE
1616.8 (179.8)
4
42.2
(13.5)
26.4 (22.3)
Garmin
vivofitd
wrist
1630.2 (234.8)
0
39.4
(12.9)
25.2 (23.0)
TANITA
AM-160d
pocket
1410.4 (211.5)
1
42.6
(14.3)
29.3 (29.0)
Omron
CaloriScand
pocket
1291.7 (186.2)
1
42.6
(14.3)
29.3 (29.0)
Withings
Pulse O2d
waist
1608.9 (228.4)
1
45.5
(13.2)
33.5 (30.8)
Omron
Active style
Prod
waist
1304.5 (188.5)
0
43.1
(13.8)
30.6 (31.3)
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4.0 Conclusions
This report discussed the integration between systems in healthcare and other related
operations systems was discussed in this report. It also provided a detailed survey on
wearable medical devices data in health information system. The interoperability of these
data within several institutions was found to be an important issue as in healthcare for
individuals and communities. Therefore, it is evident that there is a necessity to create a
dynamic technical environment for the medical informatics professional to help them in
the knowledge acquisition and support their learning process.
References
Progressive Charlestown blog (AUGUST 11, 2018), “Are they Worth it? Wearable
devices: Useful medical insights or just more data?”, available online on:
http://www.progressive-charlestown.com/2018/08/are-they-worth-it.html, accessed on
November, 29th 2019
Peckham C., Medscape EHR report (August 2016), „2016: Physicians Rate Top EHRs“
accessed online on: http://www.medscape.com/features/slideshow/public/ehr2016.,
accessed on November, 29th 2019
The medical economics bolg (October 25, 2017), 2017 EHR Report Card, available online
on: https://www.medicaleconomics.com/medical-economics-blog/2017-ehr-report-card,
accessee on November, 29th, 2019.
Lintern, G., & Motavalli, A. (2018). Healthcare information systems: the cognitive
challenge. BMC medical informatics and decision making, 18(1), 3.
Amalberti, R., & Vincent, C. (2016). Safer Healthcare: Strategies for the real world.
Springer.
Fortino, G., Galzarano, S., Gravina, R., & Li, W. (2015). A framework for collaborative
computing and multi-sensor data fusion in body sensor networks. Information Fusion, 22,
50-70.
Coiera, E. (2015). Guide to health informatics. CRC press.
Khalifa, M. (2013). Barriers to health information systems and electronic medical records
implementation. A field study of Saudi Arabian hospitals. Procedia Computer Science, 21,
335-342.
National Committee on Vital and Health Statistics (NCVHS) Report on Uniform Data
Standards for Patient Medical Record Information, July 6, 2000, pp. 21-22.
Piwek, L., Ellis, D. A., Andrews, S., & Joinson, A. (2016). The rise of consumer health
wearables: promises and barriers. PLoS medicine, 13(2), e1001953.
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