Category: Management

Question 13
Parenteral medication doses of less than 1 mL are normally measured with a 1-mL TB syringe with the exception of
a. vitamins
b. heparin
c. U-100 insulin
d. antiemetics

Prescribed:
epinephrine 0.05-2 mcg/kg/dose every 10-15 minutes
Client weight:
130 lb
What is the maximum amount (mcg) per dose the nurse may administer? Round to the nearest tenth.

When multiple doses of medication Are loaded int one drawer and they have different expiration dates, how dO you determine the expiration date that should be recorded for that drawer

Explain AHIMA’s data quailty management model, including the domains it covers and the data characterists. Provide examples that illustrate your understanding of data characteristics
Overview
The AHIMA Data Quality Management (DQM) model is a practical framework used to manage and improve the quality of health information across the information lifecycle. It frames data quality work around domains of activity (where quality is created or affected) and a set of data characteristics (attributes used to measure and describe quality). The model is intended to support governance, measurement, and improvement efforts so data are fit for clinical, administrative, and research use.
Core domains the model covers
Application/Use:
How data are used by clinicians, coders, researchers, and administrators; ensures data meet end-user needs.
Collection:
Points and processes of data capture (forms, EHR entry, devices); focuses on standard capture methods, data entry rules, and workflow design.
Warehousing/Storage/Transport:
How data are stored, transmitted, consolidated, and protected (including interfaces, repositories, and conversions).
Analysis/Reporting:
How data are processed into usable information—aggregation, reporting, analytics, and decision support.
Common data characteristics (attributes) used to assess quality
AHIMA and allied health information frameworks commonly reference a set of characteristics you can use to evaluate and manage data quality. Typical characteristics include:
Accuracy:
Data correctly represent the real-world value (e.g., recorded blood glucose equals measured value).
Completeness / Comprehensiveness:
Required data elements are present (e.g., problem list contains all active diagnoses).
Consistency:
Data are the same across systems and over time (e.g., units of measure and coding conventions match between lab and EHR).
Timeliness / Currency:
Data are available when needed and reflect current conditions (e.g., allergy updates are available before medication orders are processed).
Accessibility:
Authorized users can retrieve data when and where needed while protecting privacy (e.g., clinicians can access recent discharge summaries).
Definition / Interpretability:
Data elements have clear definitions and value sets so users interpret values correctly (e.g., a defined list of smoking status codes).
Granularity:
Level of detail is appropriate (e.g., diagnosis coded to the specific subcode rather than a nonspecific category when required).
Precision:
Data recorded to the needed level of exactness (e.g., timestamps to minute vs. only date when time matters).
Uniqueness:
Records represent one entity without unintended duplication (e.g., single consolidated patient record rather than duplicate medical record numbers).
Validity:
Data values fall within acceptable ranges or formats (e.g., birth date not in the future; lab values within physiologic or instrument ranges).
Relevancy:
Data collected are pertinent to the intended use (e.g., collecting social determinants data when needed for care planning).
Examples illustrating data characteristics
Accuracy:
A patient’s medication list matches the community pharmacy fill history; discrepancies trigger reconciliation to correct the EHR list.
Completeness:
A surgical discharge summary includes all required fields (procedure codes, blood loss, implant details); missing fields delay coding and billing.
Consistency:
Lab results transferred from the lab system to the EHR preserve units (mg/dL) and reference ranges so trends are comparable across visits.
Timeliness:
A positive culture result is entered and routed to the treating team within hours, enabling prompt antimicrobial therapy adjustments.
Accessibility:
Authorized clinicians can pull prior imaging reports from the enterprise repository during a clinic visit without special requests.
Definition/Interpretability:
“Smoking status” uses a standardized code set (never/former/current) so population health queries are meaningful and consistent.
Granularity:
Coding diabetes as E11.65 (Type 2 diabetes with hyperglycemia) rather than a generic diabetes code supports more precise analytics and care interventions.
Precision:
Recording medication administration times to the minute supports accurate assessment of time-sensitive interventions (e.g., door-to-needle times).
Uniqueness:
Master patient index processes detect and merge duplicate patient records so care history appears under one patient identifier.
Validity:
An intake form prevents entry of an impossible height (e.g., 12 meters) by validating input ranges and formats.
Relevancy:
Collecting pregnancy status for women of childbearing age prior to ordering teratogenic imaging ensures relevant clinical screening data are present.
Implementing and operationalizing the model
Establish data governance and stewardship roles aligned to the domains (who owns collection rules, warehousing standards, etc.).
Define and document data element definitions, permissible values, and capture workflows to improve definition, validity, and consistency.
Use automated checks and dashboards to measure characteristics (e.g., completeness rates, duplication rates, timeliness metrics) and monitor trends.
Provide training and feedback loops to clinicians and staff where collection and capture practices impact quality.
Apply remediation workflows (data correction, interface adjustments, mapping fixes) tied to the domain where problems originate.
In summary, AHIMA’s DQM approach links domains of the information lifecycle with measurable data characteristics so organizations can target governance, processes, and technical solutions to improve data quality and support reliable use of health information.