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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Systems Management | 27% | - Systems Analysis and Design
|
| Topic 2: Related Topics | 20% | - Healthcare Reform and Trends
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| Topic 3: Technology Environment | 27% | - Data and Information Management
|
| Topic 4: Healthcare Environment | 26% | - Healthcare Data and Information
|
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NEW QUESTION # 65
To enhance patient safety, which of the following abbreviations should be eliminated when introducing or upgrading an Electronic Health Record (EHR)?
Answer: C
Explanation:
The abbreviation "qd" (intended to mean "every day") should be eliminated because it is well known to be error-prone and has been repeatedly associated with misinterpretation and serious medication dosing errors. In handwritten or poorly rendered text, "qd" can be mistaken for "q.i.d." (four times daily), which can lead to a fourfold dosing frequency error -a high-risk patient safety event. Because EHR implementations often standardize order sets, medication dictionaries, and clinical documentation templates, this is a key opportunity to remove unsafe abbreviations and replace them with fully spelled-out, unambiguous instructions (e.g.,
"daily").
In contrast, NPO ("nothing by mouth"), PRN ("as needed"), and HS ("at bedtime") are common clinical abbreviations that are generally understood and are not typically singled out in major "do-not-use" abbreviation lists in the same way "qd" is. Safety-focused informatics practice emphasizes embedding these standards directly into computerized provider order entry (CPOE) and order sentences so clinicians select clear, standardized terms instead of typing free-text abbreviations. Eliminating "qd" supports safer prescribing, reduces ambiguity across care teams, and strengthens medication safety during EHR go-lives and upgrades.
NEW QUESTION # 66
What public health benefit can be derived from data collected from social media and internet search engines?
Answer: C
Explanation:
Data from social media and internet search engines can provide a public health benefit through the revelation of associations and patterns (Option D). These data sources are often high-volume, rapidly generated, and reflective of real-time behaviors-such as symptom searching, discussions of illness, medication side effects, or concerns about local outbreaks. When analyzed appropriately, they can help identify emerging trends , detect unusual clusters of symptoms, and signal potential outbreaks earlier than traditional reporting pathways that depend on clinical visits, laboratory confirmation, and formal case reporting. Pattern and association discovery is a core capability of analytics and informatics: mining large datasets to find relationships (e.g., increases in searches for "fever and cough" correlated with rising influenza-like illness) and temporal
/geographic trends that support situational awareness and targeted interventions.
The other options are less directly tied to a public health "benefit." Data visualization (A) and statistical analysis (B) are methods that can be applied to many datasets but do not describe the specific actionable value derived from these unconventional sources. Discovering data types (C) is a technical characterization and not a direct public health outcome. In contrast, identifying patterns and associations can inform earlier surveillance, resource planning, risk communication, and focused prevention strategies-making D the best answer.
NEW QUESTION # 67
Effective health information exchange requires:
Answer: D
Explanation:
Effective health information exchange (HIE) fundamentally depends on accurate patient identification , which is achieved through a reliable Master Patient Index (MPI) . An MPI is a core component of interoperability infrastructure that maintains unique identifiers for patients across different systems and organizations. When health data is exchanged between hospitals, clinics, laboratories, and other entities, the receiving system must correctly match the incoming data to the appropriate patient record. Without accurate patient matching, there is significant risk of duplicate records, overlay errors (information assigned to the wrong patient), incomplete clinical histories, and potential patient safety events.
Remote patient monitoring and clinical decision support are valuable digital health capabilities, but they are not foundational requirements for HIE functionality. Transcription software efficiency relates to documentation workflow and does not directly impact cross-organizational data exchange. In contrast, MPI accuracy ensures that demographic data elements-such as name, date of birth, address, and other identifiers-are properly reconciled to support safe and reliable interoperability.
Within healthcare information systems management, strong MPI governance, standardized demographic data capture, and ongoing data quality monitoring are essential best practices. Therefore, Master Patient Index accuracy is the critical requirement for effective health information exchange.
NEW QUESTION # 68
An emergency department requested a study of laboratory turn-around times. A review shows peak patient arrivals during weekend evening hours. When should sampling of turn-around occur to obtain the MOST reliable data?
Answer: A
Explanation:
To obtain the most reliable laboratory turnaround time (TAT) data for an emergency department, sampling must be representative of the full operating reality , not concentrated only in one high-volume window.
Although the review shows peak arrivals during weekend evenings , TAT performance is influenced by multiple time-dependent factors: staffing levels in the ED and lab, specimen transport coverage, analyzer workload, competing inpatient priorities, courier schedules, and shifts/hand-offs. If sampling occurs only on weekend evenings (or only on weekends), the study risks systematic bias by over-representing peak congestion conditions and under-representing baseline performance during non-peak periods.
Therefore, sampling across varied weekday and weekend hours produces the most reliable dataset because it captures both peak and non-peak operations, different staffing patterns (day/evening/night), and weekday- versus-weekend workflow differences. This broader sampling supports stronger conclusions about true average performance, variability, and whether delays are isolated to peak demand periods or occur across the week. It also enables better root-cause analysis (e.g., shift-related bottlenecks, transport gaps, batching behavior) and more credible improvement recommendations. Random weekend-only sampling or intermittent peak-only sampling may be easier, but it is less representative and therefore less reliable for organization- wide decisions.
NEW QUESTION # 69
A CEO asks the CIO to show the return on investment (ROI) for mobile technology. Equipment maintenance costs have decreased 30%, personnel costs have increased 2%, and communication costs have increased 5%.
Which of the following BEST illustrates the economic impact of the technology?
Answer: D
Explanation:
A bar chart is the best choice because the CEO needs a clear, direct comparison of discrete cost categories and their percentage changes after implementing mobile technology. In ROI and economic-impact reporting for health IT, leaders typically want to see how different cost components move in opposite directions (e.g., maintenance down while personnel and communication rise). A bar chart makes these contrasts immediately visible by placing each category on the same scale and displaying positive and negative changes side-by-side, supporting fast executive interpretation and decision-making.
A spider (radar) diagram is better suited for comparing multiple performance dimensions across a common scale (often used in capability or maturity comparisons), but it is less intuitive for conveying simple cost deltas. A scatter plot is used to show relationships or correlation between two variables (e.g., staffing levels vs. throughput), not categorical ROI components. A Pareto chart ranks causes by frequency or magnitude to highlight the "vital few" contributors to a problem; it is most appropriate when analyzing defect types or drivers of cost, not when simply communicating pre/post percentage changes across several budget lines.
NEW QUESTION # 70
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