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Evaluating Information Technology Systems for Health Information Management (HIM)
Amelia Brown
Strayer
Health Information Systems
Donna Levesque
08-07-2023
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Evaluating Information Technology Systems for Health Information Management (HIM)
Health Information Management (HIM) reflects the systemic utilization of digital Big
Healthcare data coupled with health information technology. The primary context is through the
acquisition, safekeeping, analysis, and conveyance of specific health information to various
healthcare stakeholders such as patients, multidisciplinary collaborative care teams, and health
insurance companies (Zhao et al., 2020). One advantage of HIM involves the steering of patient
engagements to improve their involvement in their healthcare process. Another advantage centers
on the improved aptitude of HIM to steer evidence-based clinical decision-making within
multidisciplinary collaborative care teams. Another merit centers on the generated aptitude for
facilitating robust insights for forecasting illness trends and evaluating the efficacy of pertinent
treatment interventions (Zhao et al., 2020).
Selecting an Information Technology System that utilizes Health Information Management
(HIM) for Facilitating Improved Patient Safety
Clinical Decision Support Systems (CDSS) mirror an example of an Information
Technology system that utilizes Health Information Technology for elevating patient safety. A
Clinical Decision Support System (CDSS) is reflective of an Information Technology framework
that oversees the analysis of Big Patient Health Information for helping multidisciplinary
collaborative care teams make informed patient-centered decision-making (Han et al., 2023). The
reason for selecting Clinical Decision Support Systems is founded on its aptitude for placing
patients at the forefront of their care which improves the attainability of individualization of their
care process. On this note, the CDSS system steers Health Information Management (HIM) via a
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comprehensive leveraging of patient-unique digital healthcare data that is also informed by the
use of scientific evidence for a continuum of quality healthcare outcomes (Han et al., 2023).
Clinical Decision Support Systems necessitate computable-founded biomedical
awareness, individual-unique healthcare information, and a logical thinking framework. The
elements amalgamate evidence-based clinical awareness with Big Healthcare Information (Han
et al., 2023). The framework in turn helps create while at the same time conveying quality,
validated, and error-free patient-specific healthcare data to multidisciplinary collaborative care
teams as the healthcare process is ongoing. The individual-specific healthcare data, through the
CDSS framework, undergoes comprehensive filtration, management, and conveyance which
helps improve the ongoing care process (Han et al., 2023). As a result, there is the generated
aptitude for prompt evidence-based decision-making that is considerate of all the patient’s needs
for person-centeredness.
Organizational Needs for the Requirements of a Clinical Decision Support System
Various organizational needs amalgamate three phases that will highlight the
requirements for the Clinical Decision Support System. The initial phase is the evaluation of the
necessity for quality improvement through the use of Health Information Management. On this
note, there is the highlighting of the specific healthcare challenge that should be tackled to attain
accessible, cost-effective, quality, and safe person-centered healthcare outcomes (Tun &
Madanian, 2023). The next Phase involves generating a consensus and selecting the Clinical
Decision Support System that helps tackle the healthcare challenge.
The next phase involves carrying out the implementation approach, infrastructure needs,
and training on Health Information Management of the CDSS framework that also appraises its
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continuous life cycle, performance outcomes, and room for improvement. Consequently, there
should be the selection of effective communication approaches for facilitating the
implementation of the CDSS system. Another context involves the selection of effective
leadership strategies such as transformational leadership for inspiring the organization's
personnel to buy into the quality change initiative per the underlying organizational culture (Tun
& Madanian, 2023). The element requirements are shown in the figure below.
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Software Development Life Cycle (SDLC) Impact on Organizational Needs
The Software Development Life Cycle (SDLC) is utilized for highlighting specific
development phases within the health information system that will facilitate Health Information
Management within the healthcare organization for improved patient safety and patient-
centeredness (Hamza et al., 2022). The initial phase, “Analysis”, will be central to acquiring
unique needs for all the involved stakeholders, especially patients, to understanding the nature of
their needs and how to tackle them. Additionally, the analysis phase will help evaluate the
organizational business needs and probable threats for integrating the health information
technology system (Hamza et al., 2022).
The second phase, “Planning”, will be central to highlighting the pertinent scope of the
healthcare challenge and pinpointing possible counteractive measures. On this note, there will be
the underlying various organizational needs such as infrastructure, finances, and timeframe for
project implementation (Hamza et al., 2022). The third phase, “Architecture & Design” will
encompass the High-Level Design (HLD) and Low-Level Design (LLD) sub-phases. The HLD
sub-phase will be significant for underlining the long-term architectural aspects of the health
information technology system. On the other hand, the LLD sub-phase will be central to
highlighting the architectural elements that should oversee improved patient safety (Hamza et al.,
2022).
The fourth phase, "Development", encompasses the actual development of the specific
Clinical Decision Support System. Clinical Decision Support Systems typically amalgamate
three-layer frameworks that enable their overall success within an organizational context (Hamza
et al., 2022). The primary layer is the “Data Management Layer” which integrates the evidence-
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based clinical database for acquiring, curating, analyzing, and conveying healthcare Big Data
(AltexSoft, 2020). Healthcare Big Data may be in the form of patient-unique healthcare
information, illness trends, and laboratory findings. The “Data Management Layer” acts as the
knowledge level that constitutes the "If-Then” protocol for machine learning (altexsoft, 2020).
The second layer is the "Inference Engine" which is utilized for applying standardization
(algorithms) coupled with data within the knowledge base for the availability of patient-unique
healthcare information. The outcomes are conveyed and revealed within the uppermost layer, the
"User Interface Layer" (altexsoft, 2020). The User interface layer constitutes various applications
that may be in hardware or software form. Examples of elements within the User Interface Layer
include smartphones, smartwatches, desktop applications, mobile text alerts, and an Electronic
Healthcare Records framework (altexsoft, 2020).
The fifth phase, "Testing" will enable actual testing of the Clinical Decision Support
System to prove its efficacy for improved patient safety and person-centeredness. The sixth
phase, "Maintenance", will encompass routine performance appraisal for ascertaining the need
for improvements and better performance (Hamza et al., 2022).
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Data and Security Breach Vulnerabilities for Clinical Decision Support Systems and
Counteractive Measures
A possible data and security breach vulnerability for the Clinical Decision Support
System that is utilized within Health Information Management is Shadow IT. Shadow IT mirrors
the utilization of unofficial third-party software, apps, or even internet services with the CDSS
system and is usually not identified by the Information Technology personnel (Tariq et al., 2023).
Shadow IT may generate challenges such as the unwarranted sharing of patients' private and
confidential information without their approval. A significant approach for countering Shadow IT
involves facilitating a continuum of open-ended communications between all the healthcare
organization’s personnel to comprehend secure technological ethical and compliance adherence.
Additionally, there can be integration of Data Loss Prevention (DLP) apparatuses that halt
known or unknown integration of Shadow IT within the Clinical Decision Support System (Tariq
et al., 2023).
Phishing coupled with social engineering is another data and security breach vulnerability
for the Clinical Decision Support System. Phishing and social engineering mirrors when the IT
technology system users tend to be hoodwinked into facilitating a harmful online activity such as
giving away patients’ personal information. Phishing and social engineering can be tackled by
installing antivirus software that is regularly updated as newer attacks are developed each second
(Tariq et al., 2023). Additionally, health information technology personnel are necessitated to be
routinely trained on how to stay vigilant to prevent possible phishing and social engineering via
the CDSS system.
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Physical theft of the healthcare organization’s software or hardware information
technology apparatuses is another data and security breach vulnerability for the Clinical Decision
Support System. The challenge can either be faced while working within the organization’s
premises or remotely (Tariq et al., 2023). The challenge can be countered by using encryption
that prevents unauthorized access to the given apparatuses. Another option involves integrating a
“Remote Wipe Out” option for the apparatuses whenever an apparatus is misplaced or deemed
stolen which prevents unauthorized access to sensitive healthcare information (Tariq et al.,
2023).
A Synthesis of How Analysis Outcomes by SDLC on Clinical Decision Support Systems
Improves Patient Quality of Care
Using the Software Development Life Cycle (SDLC) to analyze the Clinical Decision
Support System (CDSS) enables the generation of a comprehensively functioning healthcare
information technology system. On this note, there is the enabled aptitude for generating a health
IT system that meets all the involved stakeholders’, especially patients’, needs through the in-
depth capability for analyzing healthcare Big Data to steer evidence-based decision-making
(Salsabila et al., 2021). Additionally, there is an improved aptitude for improved facilitation of
Healthcare Information Management (HIM) for person-centeredness of care. Another context
involves the generated aptitude for reducing possible threats to the system's functioning, which
helps the organization stay on track with the defined organizational culture for high-end patient
safety (Salsabila et al., 2021).
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References
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Clinical decision support systems: How they improve care and cut
costs
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systems/#:~:text=A%20typical%20CDSS%20contains%20three,typical%20clinical
%20decision%20support%20system.&text=a%20knowledge%20base%20in%20the,rules
%20or%20machine%20learning%20models.
Hamza , M., Akbar , M. A., & Alsanad , A. A. (2022). Decision-Making Framework of Require-
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Tariq, U., Ahmed, I., & Bashir, A. K. (2023). A Critical Cybersecurity Analysis and Future Re-
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