Health Management Information System (HMIS) Flashcards

1
Q

Definition of
Systems
Information system
Health information system-HIS
Health management information system -HMIS

A

System:- A collection of components that work together to achieve a common objective

Information System:-A system that provides information support to the decision-making process at each level of an organization

Health Information System (HIS):- A system that integrates data collection, processing, reporting, and use of the information necessary for improving health service effectiveness and efficiency through better management at all levels of health services.

HMIS:- am information system specially designed to assist in the management and planning of health programs as opposed to delivery of care.

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2
Q

The HMIS

A

The HMIS
•The modern health care system involves managing an enormous amount of information:-about millions of people. Only with the help of modern information technology can the health care industry provide quality care to its patients.

•In 1990s, computer use in the hospitals was limited to medical appointment schedules, but now a patient’s complete medical history is stored on them. This technology saves paper and also allows instant provider orders, shared patient data and comparison studies

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3
Q

Data collection

A

Data collection
•Different administrative levels in the health system have different roles, and therefore have different data needs.
•Not all data needs should be generated through the routine system of data collection. Data that are not frequently needed or are required only for certain subsets of the population can be generated through special studies and sample surveys.
•Other sources may be from health-related information systems under the responsibility of other agencies or institutions (birth and death, census secretariat e.t.c)

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4
Q

Routine data collection

A

Routine data collection
•At the OPD:-monthly morbidity forms (CD-2), weekly communicable disease data (CD-1).
•At the CWC:-immunization data, growth data, use of lTN
•At the ANC, maternity and PNC:- SP uptake (IPT), Hb levels, parity, age of mothers, foetal weights, SB deliveries, delivery methods, supervised deliveries etc
•School health:- enrollment, DMFt,
•Family planning:- Acceptor rates, methods of
. Choice

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5
Q

Data collection and reporting tools

A

Data collection and reporting tools
•The capability of the staff who will be tasked with filling out the forms is always important when designing the forms.
•The most effective data collection and reporting tools are simple and short.

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6
Q

Rationale for data quality

A

Rationale for data quality
•The primary function of the health information system is to provide data that enhance decision-making in the delivery of health services.
•Ensuring data quality is key in the process of supporting evidence-based decision making.

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7
Q

Determinants of data quality

A

Determinants of data quality

Data quality in the health sector is basically affected by two categories of problems

1• Inadequate accuracy, completeness and timeliness of data through the recording and reporting procedures.

2• Insufficient use of available data for health planning, delivery, monitoring and evaluation.

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8
Q

Key components necessary for ensuring data quality

A

Key components necessary for ensuring data quality
1.data collection
2.data processing (data entry, data cleaning and rearrangement of the information for analysis)
3.data analysis
4.presentation of data for use

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9
Q

Explanation of the followings

  1. Ensuring quality in data collection(causes / sources of poor quality of data may include).
  2. Ensuring Quality in data processing
  3. Ensuring quality data analysis
  4. Data dissemination and feedback
A
  1. Ensuring quality in data collection
    Causes/sources of poor quality of data may include:
    1.missing information: e.g. lack of principal diagnosis
    2.incorrect information: e.g. error in discharge date
    3.poorly documented information: e.g. preliminary diagnosis registered as final diagnosis
    4.use of non-standard abbreviation: e.g. unknown acronym used as principal diagnosis
    5.misapplication of rules for the definition of principal diagnosis e.g selection of the principal diagnosis, when multiple diseases are recorded
  2. Ensuring Quality in data processing
    •In a computerized system, most data are captured electronically with built-in checks and edits to ensure that the data added to the record are valid, therefore reducing errors.
    •Use of logical range of values “legal values” in data entry programs that are permitted for data items
    •There are programs that may not allow an entry to be added if it fails the edit
    •There is the need to verify outliers, e.g. long duration of hospital stay, high number of cases for some conditions (polio, vs malaria )
  3. Ensuring quality data analysis
    •Information is not valuable unless it is accurate, relevant, structured and presented in an easily useable form.
    •Raw data are difficult to visualize and interpret and is therefore useful to present them in tables, graphs or maps to display patterns or trends.
    •Tables are used to summarize data, and graphs and maps present them in visual form.
    •Different types of data, such as continuous and discrete data, have specific ways of graphical representation
  4. Data dissemination and feedback
    •An effective way of motivating data producers is to constantly provide them with both positive and negative feedback on the data they produce
    •There is need to determine the most effective and efficient way of disseminating the data generated from the HMIS
    •The development of the HMIS is always a work in progress. It is a dynamic endeavor where managers and workers strive for constant improvement.
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10
Q

Advantages of Tables

A

Advantages of Tables
Advantages of using tables include:
•They are easily understood
•More information can be presented
•Exact values can be read to retain precision
•Supportive details can be provided
•Easy and inexpensive to construct

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11
Q

Principles of Table Construction

*Refer to slide to see an example of the table

A

Principles of Table Construction
•Tables should be self-explanatory
•The title should be clear, concise, and answering the questions: what? when? where?
•Each row and each column should be labelled concisely and clearly
•The specific units of measure for the data should be given
• codes, abbreviations, or symbols should be explained in a footnote
•Totals should be shown in the table

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12
Q

Graphical Presentation

A

Graphical Presentation
A graph is used as a pictorial representation of data and in most cases proves the best medium for presenting raw data.
Advantages of using graphs include:
•They are easily understood
•They bring out hidden facts
•They display trends (patterns) and comparisons more vividly
•They are attention getters.

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13
Q

Essential component of Graphs

A

Essential component of Graphs
•Generally, the variable assigned to the x-axis is considered the independent variable
•The variable assigned to the y-axis is the dependent variable (frequency).
•In drawing a graph, we plot a change in “y” with respect to “x”
•The title must relate what the graph shows as simply as possible and the labels should be clear, complete, and easy to understand
•When more than one variable is shown on a graph, it is necessary to identify each by using keys
. The specific unit of measure for the data should be given and the source of data should be given at the footnote

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14
Q

Choosing Type of Graph

For continuous data set use 3
For discrete data set use 1
For other graphs use 3

A

Choosing Type of Graph

Type of graph depends on the type of data.
•For continuous data set use
- Histogram
- Frequency polygon
- Line graph
•For discrete data set use
- Bar diagram
•Other graphs
• Pie chart
. Spot mat
. Scatter diagram

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15
Q

Histogram

Refer to slide for e.g

A

Histogram
A histogram is used to present the frequency distribution of a continuous data set.
•The continuous variable (e.g. age) is put on the horizontal axis, and the frequencies (e.g. number of cases) on the vertical axis.
•Adjacent columns are not separated by space, showing that the scale in the horizontal axis is a continuous measurement scale. The common type of histogram is with equal class intervals

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16
Q

Frequency polygon

A

Frequency polygon
•A frequency polygon is often used to represent two or more distributions superimposed for easy comparison on one graph.
•The columns of the histogram can be converted into lines of the frequency polygon by joining the midpoints of the intervals, so that two or more distributions can be shown.

17
Q

Line graph

A

Line graph
•The line graph is used to show the change of a variable in relation to another time-related variable, such as month or year.
•The time-related variable (i.e. month) is plotted along the horizontal axis and the values of the “dependent” variable (i.e. number of cases) on the vertical axis.

18
Q

Bar chart
e.g in slide

A

Bar chart
•Bar charts are used to represent a discrete data set. The classes (disease) are put on the horizontal axis, and the frequencies on the vertical axis.
•The frequency of each group is represented by the length of the corresponding bar, and all bars are characterized by an identical column width.
•Bar charts resemble the histograms in appearance, but they differ because the bars are separated by a space.

19
Q

Pie Chart

A

Pie Chart
•Pie chart is a circular diagram cut up into pieces to represent relative (proportionate) frequencies for comparison between groups.
•The pie chart is best adapted for illustrating the division of the whole into segments.
•The convention is to start at the 12 o’clock position and arrange segments in the order of their magnitude, largest first, and proceed clockwise around the chart.

20
Q

The data flow

A

The data flow
•Not all data collected at a certain level need to be submitted to higher levels. The most detailed data are kept at the source and those to higher levels are kept at a minimum.
•There is the need to determine how frequently data should be submitted to each level
•There is the need to determine in what form data will be submitted to each level.
•> Raw data versus summaries
•> Hard copies versus electronic files

21
Q

Use of information across service ievels

A

Slides

22
Q

The decision cycle

A

Slides

23
Q

Monitoring and Evaluation of HMIS

A

Monitoring and Evaluation of HMIS
•The goal of M&E is not to focus on what is wrong and condemn it; rather, it is to highlight the positive aspects of the system as well as to identify what went wrong to improve the system.
•The steps to be taken include:
(1) Develop and implement the M&E plan.(what, how, who, resources needed etc)
(2) Document and disseminate the results of monitoring and evaluation activities.
(3) Make recommendations based on
the results of monitoring and evaluation activities

24
Q

Challenges of HMIS in Ghana’s system operation

A

Challenges of HMIS in Ghana’s system operation
•HMIS system operation
- Parallel systems/Vertical reporting systems
- Multiple data managers and reporting forms
•Results
- Inaccuracies/incompleteness /Untimeliness
•Human resource issue
- Unskilled staff/Inadequate numbers
•Technology issue
- Computers/Appropriate software

25
Q

The way forward?-solution

A

The way forward?-solution
•Development of DHIMS
- District Health Information Management System
APPROACH
- Develop a data management software
- Develop data management manuals
- Select pilot districts
- Train staff in selected districts
- Provide resource to selected districts
- Provide facilitative supervision

26
Q

Advantages of DHIMS

A

Advantages of DHIMS
•Ensures efficient use of skilled information management personnel and ICT resources.
•Eliminates parallel reporting systems
•Checks the proliferation of data collection forms.
•Enables a platform for data sharing.
•Frees programme managers from repetitive data management tasks
•Eliminates multiple data storage sites and provides a central data repository at the
- District
- Regional
- National levels (CHIM)

27
Q

Usefulness of HMIS

A

Usefulness of HMIS
•Monitoring disease patterns (Trends)
•Planning of resources (human, financial, material)
•Prediction of outbreaks and emergency preparedness
•Indicators of quality of life
•International standards (indicators)
•Advocacy tool

28
Q

Causes / sources of poor quality of data
and one example each

A

Causes/sources of poor quality of data may include:

1.missing information:
e.g. lack of principal diagnosis

2.incorrect information:
e.g. error in discharge date

3.poorly documented information:
e.g. preliminary diagnosis registered as final diagnosis

4.use of non-standard abbreviation:
e.g. unknown acronym used as principal diagnosis

5.misapplication of rules for the definition of principal diagnosis
e.g selection of the principal diagnosis, when multiple diseases are recorded