Related Experiment Video
Updated: Jan 24, 2026
07:17
Leveraging Virtual Reality for Immersive Segmentation and Analysis of Cryo-Electron Tomography Data
Published on: January 24, 2025
1.4K
Leveraging existing program data for routine efficiency measurement in Zambia
Rick Homan1, John Bratt1, Gregory Marchand2
1Health Services Research, FHI 360, Durham, North Carolina, 27701, USA.
Gates Open Research
|May 28, 2019
Summary
Routine Efficiency Monitoring System (REMS) improves health resource allocation for HIV/AIDS services. This system provides crucial unit expenditure data, enabling better efficiency measurement and targeted interventions for sustainable health programs.
Area of Science:
- Health economics
- Public health systems
- Health informatics
Background:
- Global donor funding for HIV/AIDS is stagnating, necessitating improved resource utilization within national health systems.
- Program managers and planners lack routine data on unit expenditures and resource use at the facility level, hindering efficiency assessment.
- Measuring technical efficiency and evaluating interventions to improve it are challenging without granular cost and output data.
Purpose of the Study:
- To develop and implement a Routine Efficiency Monitoring System (REMS) to estimate unit expenditures per service at the facility level.
- To measure the technical efficiency of Ministry of Health resources used for HIV/AIDS service delivery in Zambia.
- To provide data for identifying inefficiencies and guiding resource allocation decisions.
Main Methods:
- Developed REMS, a relational database, to integrate existing budget, expenditure, and output data.
- Configured REMS for 326 facilities in Zambia, focusing on HIV/AIDS services, in collaboration with the Government of the Republic of Zambia (GRZ) and Avencion.
- Utilized facility assessments and key informant interviews to develop REMS allocation algorithms, drawing data from IFMIS and DHIS-2.
- Delivered REMS outputs via user-friendly, web-based dashboards for trained users.
Main Results:
- REMS provides quarterly site-level estimates of unit expenditure per service, enabling measurement of technical efficiency.
- District health managers can identify "outlier" facilities for targeted performance improvement interventions.
- Provincial and national planners gain insights to direct resources to areas of greatest need and identify potential savings.
Conclusions:
- REMS significantly enhances data availability for managers and planners on resource transformation into services.
- The GRZ is seeking national expansion of REMS to cover all major disease areas.
- Improved technical efficiency through REMS supports the goal of a sustainable HIV/AIDS response.
Related Concept Videos
00:59How Data are Classified: Numerical Data
37.1K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
37.1K
01:11How Data are Classified: Categorical Data
43.1K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
43.1K
01:24Data Reporting and Recording
5.4K
Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
5.4K
01:30Data Collection I
7.9K
Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
7.9K
01:03Data Validation
6.4K
Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
Nursing assessment guides are generally based on holistic models rather than medical...
6.4K
01:15Data Validation
713
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
Key parameters for method validation include:
713