Please read and contribute to peer discussion in 100 words with at least 2-3 credible references in APA style.

Peer 1

Strong, robust systems for capturing health program data are essential to tracking progress toward health objectives, such as the Millennium Development Goals, and will be central to supporting data-informed decisions as part of the new Sustainable Development Goals. The data quality assessment tools were originally developed as part of global efforts to combat AIDS, malaria, and tuberculosis. Ambitious plans for national programs and donor-funded projects were in the works to reduce the burden of disease in countries around the world. Measuring the success and improving the management of these initiatives is predicated on strong monitoring and evaluation (M&E) systems that produce good-quality data related to program implementation. In the spirit of the “Three Ones,” the “Stop TB Strategy,” and the “Roll Back Malaria Global Strategic Plan,” a number of multilateral and bilateral organizations collaborated to develop the Data Quality Audit (DQA) Tool. This tool captures high-priority indicators from HIV and AIDS, tuberculosis, and malaria programs and offers a common approach to assessing and improving overall data quality. Having a single tool helps to ensure that standards are harmonized and allows for joint implementation by partners and national programs. Implementing the DQA tool revealed the need for a capacity-building and self-assessment version. To that end, MEASURE Evaluation (funded by the U.S. Agency for International Development), the World Health Organization, the U.S. President’s Emergency Plan for AIDS Relief, and the Global Fund to Fight AIDS, Tuberculosis and Malaria worked together to develop the Routine Data Quality Assessment (RDQA) Tool. We designed it to build the capacity of health programs to assess and improve the quality of their data. The tool has subsequently been applied many times—both by individual health programs and by country health management information systems (HMIS). The RDQA tool verifies the quality of reported data and assesses the underlying data management and reporting systems for standard program-level output indicators.

Purpose: Performance standards for birth defects surveillance are intended to improve and standardize operations, outcomes, and surveillance functions across state programs, thereby making data more consistent and useful for a variety of purposes at local, state, multi-state, and national levels.

Format: This assessment tool lists performance indicators and associated measurements for data quality. Each line item measurement identifies the level of standards performance. Performance indicators are organized into completeness, timeliness, and accuracy categories.

Definition-explanations: Each performance indicator is followed by the definition that provided clarification of how to interpret the indicator and the reason/explanation for the specific performance indicator. (Data Quality Assessment Tool, n.d.)

Data quality assessment tool for birth defects surveillance:

Check the highest Performance level that applies.. Level1 – Unable to achieve Level3 – Achieved

CompletenessLevel 1Level 2Level 3
Types of data sources are used systemically and routinely to identify potential cases at the population-based level.Level 1Level 2Level 3
Birth defects included using standard definitionsLevel 1Level 2Level 3
Pregnancy outcomes includedLevel 1Level 2Level 3
Systematic and routine identification of cases during ascertainment period (age of diagnosis)Level 1Level 2Level 3
Data elements collectedLevel 1Level 2Level 3
TimelinessLevel 1Level 2Level 3
Time of case data completion for “core” listLevel 1Level 2Level 3
Time of case data completion for “recommended” listLevel 1Level 2Level 3
AccuracyLevel 1Level 2Level 3
Data quality procedures for verification of case diagnosisLevel 1Level 2Level 3
Scope of birth defects verifiedLevel 1Level 2Level 3
Level of expertise for individuals who perform case diagnosis verificationLevel 1Level 2Level 3
Database quality assurance processLevel 1Level 2Level 3


Data Quality Assessment Tool. (n.d.). National Birth Defects Prevention Network. Retrieved February 3, 2022, from

Routine Data Quality Assessment Tool – User Manual — MEASURE Evaluation. (n.d.). MEASURE Evaluation. Retrieved February 3, 2022, from

Peer 2

The paper-based medical record has been largely being replaced in most United States (U.S.) healthcare facilities with the Electronic Health Record (EHR). The EHR is like a paper one, except all the information gets stored electronically. The EHR has the same components necessary for the patient’s information. The components are and are not limited to name, date of birth, gender, marital status, demographics address. Additional components included insurance information, vital signs, diagnoses, medical history, immunization dates, progress notes, lab data, imaging reports, and allergies. The purpose of EHR is to ensure that the patients receive the best quality of care. In the U.S. if a person has been seen by a healthcare provider a medical record will and has been created and all this information is called data (Digital Health, 2020) Data is formulated by multiple providers along the patient’s care continuum all data must have integrity, be reliable and be accurate. The Health Information Management (HIM) staff ensures that all data in the EHR is reliable and accurate. The American Health Information Management Association (AHIMA along with the AHIMA Governance Principles for Healthcare (IGPHC) enacted key principles for data information (AHIMA, 2015). The measures of data quality are Accountability, Transparency Integrity, Protection, Compliance, Availability, Retention, and Disposition. Oachs & Watters,( 2020) “ healthcare delivery systems are increasingly using quality metrics to benchmark and assess the quality of care, and inform improvement initiatives”. In conclusion, the best practice for the HIM department to achieve “data goals” of integrity, reliability, and accuracy is the have a Data Quality Tool Kit (DQTK) for EHR as indicated below.

Data Quality Tool Kit

Data Quality CharacteristicsData Measure(s)Select Yes or NoThe record meets the data quality measureComments
Data AccuracyData is free of errorsIs the patient’s name on every page?Is the patient’s date of birth correct and gender correct?Yes———No—–
Data TimelinessAll data is up to date in the EHR, within healthcare’s determined time frame,Is the History & Physical documented within 24 hours of admission? Is the Discharge summary written no later than 24 hours post-discharge?Yes———No—-
Data ComprehensivenessEach patient encounter is documented and in the EHR.Are all patient encounters accounted for, date, time, and signed by the appropriate provider or clinician?Yes———No—–
Data CurrencyAll data is up to date and the EHR is assessable and availableCurrent admission notes are documented and in the EHRYes———No—–


American Health Information Management Association (2015). Data Quality Management Model (2015 Update) Retired.…

Digital Health (2020).…

Oachs, P. K., & Watters, A. L. (2020). Health information management: Concepts, principles, and practice (6th ed.). AH

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