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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Related Topics | 20% | - Healthcare Reform and Trends
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| Topic 2: Systems Management | 27% | - Systems Analysis and Design
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| Topic 3: Healthcare Environment | 26% | - Clinical and Business Terminology
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| Topic 4: Technology Environment | 27% | - Applications and Software
|
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NEW QUESTION # 87
A risk response plan includes swapping desktops for laptops for physicians to eliminate the risk of physicians failing to adopt a new Electronic Health Record (EHR). This is an example of
Answer: B
Explanation:
Risk avoidance involves changing a project plan to eliminate a threat entirely , rather than merely reducing its probability or impact. In this scenario, leadership identifies the risk that physicians may resist or fail to adopt the new EHR system due to workflow inconvenience or lack of mobility. By replacing desktop computers with laptops, the organization alters the work environment to remove a key barrier to adoption-thereby eliminating the root cause of the identified risk. This proactive adjustment represents risk avoidance because it restructures the approach so that the risk condition no longer exists in its original form.
Risk mitigation, by contrast, would reduce the likelihood or impact of non-adoption (for example, through training or support programs) but would not fully remove the underlying barrier. Risk transference shifts responsibility to another party (such as through insurance or outsourcing). Risk acceptance acknowledges the risk without taking preventive action.
Within healthcare IT governance and project management frameworks aligned with HIMSS principles, risk avoidance is appropriate when the organization can feasibly change scope, technology, or workflow to eliminate a significant adoption threat. Ensuring clinician engagement and usability is critical for EHR success, and structural changes that remove adoption barriers exemplify risk avoidance.
NEW QUESTION # 88
Data mining
Answer: B
Explanation:
Data mining refers to the analytical process of examining large datasets to discover hidden patterns, correlations, trends, and relationships that are not immediately apparent through routine reporting. In healthcare information and systems management, data mining plays a critical role in transforming raw clinical, financial, operational, and administrative data into actionable knowledge. Using statistical algorithms, machine learning techniques, clustering, classification, association rule discovery, and predictive modeling, healthcare organizations can uncover insights such as risk factors for readmissions, patterns of medication utilization, disease prevalence trends, fraud detection indicators, and workflow inefficiencies.
Option A describes simulation modeling, which is a different analytical method used to replicate processes for testing scenarios. Option B refers to data warehousing or database management systems, which focus on storage rather than analysis. Option C more closely aligns with predictive analytics or formal research methodology, not specifically data mining itself.
Within healthcare IT governance and HIMSS-aligned informatics principles, data mining supports evidence- based decision-making, quality improvement initiatives, population health management, and strategic planning. By revealing previously undetected relationships in large datasets, healthcare leaders can improve patient outcomes, enhance operational efficiency, reduce costs, and support regulatory reporting requirements.
NEW QUESTION # 89
Which of the following is MOST important to ensure successful data integration between two systems?
Answer: A
Explanation:
Successful data integration depends first on shared meaning of the data being exchanged. A common data dictionary provides the agreed-upon definitions, formats, permissible values, units of measure, and identifiers for data elements (for example: patient identifiers, encounter numbers, provider IDs, lab test codes, medication codes, and timestamps). Without this shared semantic foundation, two systems may exchange data correctly from a technical standpoint yet still fail operationally because the receiving system interprets data differently (e.g., mismatched code sets, different units such as mg vs. mcg, inconsistent field lengths, or different meanings for "discharge date" vs. "discharge time").
While secure transmission is essential for protecting PHI (e.g., encryption in transit, authentication), it does not ensure that integrated data is accurate, comparable, or usable. The data entry process affects upstream data quality but does not resolve mapping and semantic alignment across systems. Verification of calculations is important for analytics and reporting validation, but it occurs after the underlying data elements have been defined and mapped consistently.
In healthcare information systems management, integration success is measured by correctness and usability across workflows-achieved by standardizing data definitions and mappings through a common data dictionary (often aligned with standards and code sets) before interface build and testing.
NEW QUESTION # 90
Strategic plans include
Answer: D
NEW QUESTION # 91
Digital health apps and fitness tracking devices can add patients' health data to their Electronic Health Records (EHR) by using a(n):
Answer: B
NEW QUESTION # 92
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