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MARICOPA HIS PROJECT DATA QUALITY AND MONITORING PLAN Maricopa HIS Project Document Approved by the Maricopa Continuum of Care Regional Committee on Homelessness on March 18, 2013, MARICOPA HIS PROJECT
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How to fill out hmis data quality plan

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How to fill out an HMIS Data Quality Plan:

01
Start by understanding the purpose of a HMIS Data Quality Plan. It is a document that outlines the strategies and procedures for ensuring the accuracy, completeness, and reliability of data collected in the HMIS system.
02
Begin by determining the key stakeholders who will be involved in the development and implementation of the plan. This typically includes representatives from the HMIS lead agency, data entry staff, system administrators, and program directors.
03
Collaboratively establish goals and objectives for the Data Quality Plan. These may include improving data entry accuracy, reducing data duplication, and enhancing overall data quality.
04
Identify the specific data elements that will be monitored and assessed for quality. This can include demographic information, program participation details, and outcomes data.
05
Develop data entry protocols and guidelines that will help ensure consistent and accurate data collection. This can involve creating standardized forms, establishing data validation rules, and providing training to data entry staff.
06
Define data quality thresholds and establish benchmarks for measurement. This involves setting acceptable ranges or parameters for data accuracy, completeness, and timeliness.
07
Determine the frequency and method for conducting data quality checks. This can involve both automated and manual processes, such as running system-generated reports, conducting sample audits, and performing data checks against external sources.
08
Develop a corrective action plan to address any identified data quality issues. This should include specific steps for remediation, assigning responsibilities, and establishing timelines for resolution.
09
Establish a process for regular monitoring and reporting on the progress of the Data Quality Plan. This can involve creating dashboards, conducting regular meetings with stakeholders, and providing ongoing training and support to data entry staff.

Who needs an HMIS Data Quality Plan:

01
HMIS lead agencies: They are responsible for overseeing the implementation and maintenance of the HMIS system, making them key stakeholders in the development of a Data Quality Plan.
02
Data entry staff: Those individuals who are responsible for entering data into the HMIS system need a Data Quality Plan to guide their efforts and ensure that accurate and reliable information is being collected.
03
System administrators: They play a crucial role in maintaining the HMIS system and should be involved in the development and implementation of the Data Quality Plan to ensure its integration into the overall system infrastructure.
04
Program directors: They rely on the data collected in the HMIS system to make informed decisions and evaluate the effectiveness of their programs. They need a Data Quality Plan to ensure the accuracy and reliability of the data used for reporting and analysis.
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HMIS data quality plan is a document outlining processes and procedures to ensure the accuracy, consistency, and completeness of data collected and reported in the Homeless Management Information System.
All organizations or agencies that are required to submit data to the HMIS are required to file an HMIS data quality plan.
HMIS data quality plan can be filled out by reviewing and following the guidance provided by the HMIS administrator or coordinator. It typically involves documenting data collection processes, establishing data quality standards, and outlining procedures for data validation.
The purpose of the HMIS data quality plan is to ensure the reliability and integrity of data collected in the HMIS, which is essential for making informed decisions, measuring outcomes, and tracking progress in addressing homelessness.
The HMIS data quality plan should include details on data collection methods, data entry procedures, data validation processes, data quality assurance activities, and strategies for addressing data quality issues.
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