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8/3/2019 Public Health Management-An Analytical Approach Using GIS Based Knowledge Management-Atul Asthana
http://slidepdf.com/reader/full/public-health-management-an-analytical-approach-using-gis-based-knowledge-management-atul 1/14
Public Health ManagementAn analytical approach using GIS based knowledge management
Abstract
1. In third world countries, where the health facilities are far and few, public health
management systems need all the support they can get from which ever quarter. Use
of Information Technology can greatly assist improvement in healthcare, from basic
patient registration to exchange of information amongst practitioners of health
management. One of the major steps is to use IT in information collation, analysis and
creating decision support systems for health practitioners. A further dimension can be
added by using spatial and non-spatial data to create a knowledge management
system and provide holistic approach to managing public health. A GIS can be used as
a data repository. Simple IT enabled patient registration systems can feed information
into the GIS, on which analytical engines and knowledge systems can operate to
create decision support systems for public health professionals. Electronic
communication systems can be additionally utilised to automatically communicate
results, generate alarms and inform concerned people. This analysis can provide
insight into geographic, demographic and socio-economic factors linked to diseases,epidemics and wellness of people. The analytics engine can also predict many aspects
like disease spread, requirement of medicines, requirement of health professionals,
equipment etc. to help optimise utilisation of resources. Further data mining can help in
research on health care, geneological links to diseases etc. Additionally, this simple
system is replicable in third world countries.
2. A very basic system of using GIS with health data was created and tried using all open
source and free software.
Background : Public Health Delivery
3. In India, Public Health Management remains government's responsibility as in many
other countries world wide. In India, the healthcare system is largely provided for and
managed by government, where large part of the population tries to find succor when
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their health fails. Public hospitals are always flooded with people.
4. To cater for this mass, the government employs a large number of healthcare
professionals and literally is the largest customer to the pharma industry. Public Heath
Management involves doing more than treating the patients. It involves preventing
people from being afflicted with diseases. Thus the management involves reacting to
existing situation and assessment, planning & execution for the future.
5. The planning part is based on data collected from various government healthcare
organisations (and other organisations), sorting, collating and processing the data to
generate reports.
6. Most of our existing data recording systems are based on use of paper as a medium
for recording transactions. These have two distinct short comings :
(a) Very little information can be recorded leading to recording of bare essentials.
(b) Data is less amenable to processing by automatic (electronic means), thus manual
compiling reports out of the recorded transactions takes a large amount of time
(some times over a month), is very error prone and requires multiple iterations.
Thus minimal reports are generated which are far and few e.g fig 1.
Fig 1. Monthly report(based on data collected from disparate sources and extrapolated as an example)
7. These reports are collected by management bodies from various healthcare
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organisations and may arrive at different times or may not reach at all. These reports
from are collated and letters are written to obtain any corrections or errors observed.
Thus the reports become part of statistics which can not be used for determining
current scenarios. And can obviously not be used (rather are not available) for any
planning or decision making in short time frame.
8. Other problems observed in providing good healthcare are shortage of doctors,
paramedics, equipment not working, shortage of medicines, over burdened
organisations or under utilised departments, not enough trained personnel or not
enough training.
Solving the problem to improve healthcare
9. Basic data collection on paper and report generation : Basic patient data in most of the
healthcare centers, specially the rural ones, is collected on paper. Due to this, even
reporting authorities accept the shortcoming and expect only monthly reports :
example : Monthly report (Fig 1.) and Morbidity report (Fig 2.).
10. Standardised software applications running on mobile phones or use of
computerised patient registration system could be resorted to, to over come the
problems of paper based data collection. The data could be used for generation of
basic local reports healthcare center.
11. Data collection and collation from computerised patient registration systems of
various healthcare centers could then be electronically sent to a central repository,
where data analysis could be carried out. Where ever, broadband connectivity is not
available, basic data could be sent by sms.
12. Statistical analysis of data could be carried out and reports could be generated
for making decisions. The decisions could also be communicated to healthcare
centers. Birds eye view reports could be generated for others.
13. Intelligence can be derived by correlation of health/patient data with geospatial
and non-geospatial data from GIS to link root causes to environmental conditions of
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the area.
14. This derived intelligence can be used to predict (models) spread of diseases,
likely hood of recurrences of diseases and for taking preemptive action to prevent
recurrences.
Use of Information & Communication Technology and Geographical Information
Systems
15. Present day Information Technology tools provide us the ability to record patient
data electronically. This patient data collected over a time slot viz. a day, an hour etc.
can be statistically analysed, helping in decision making. This electronic data can also
be easily and speedily communicated to others. Thus its possible to collect patient
data from various organisations located in various different locations for broader
analysis with parameters like time, age, sex, disease type etc. Using ICT tools, its also
possible to automatically communicate the electronically captured data to other
organisations.
16. A properly created, programed and live/updated Geographical Information
System can provide geographical information about the patients' habitat, environment
and other non-spatial information viz. type of dwelling, sanitation, livelihood status,
earnings, other socio-economic parameters etc. which can be used along with patients
data to create scenarios for public health management professionals which will help
them take quick, appropriate and futuristic decisions in treatment, further prevention
and future eradication of diseases. Various state governments have started efforts in
creating Geographical Information System for use by departments in their states, which
can be used for this purpose and is preferable over a stand alone GIS only for this kind
of facility. The GIS also needs to be updated from time to time, if its not being used live
by others.
17. Data collected during 2011 census, can provide socio-economic, livelihood and
disability status of each house enumerated. The census data, along with Aadhaar
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number (UID number), can be used for geo-locating and referencing every resident.
18. This basic system comprising of GIS populated with data used by government
departments, census data, Aadhar number and infrastructure details can be used for
many other purposes apart from health / public health management. In fact, even
without government department data, this basic system can be utilised for many
purposes.
Use of ICT and GIS can be at various levels of involvement
19. Level 1 : Basic data collection and report generation using IT tools, where data
is collected locally and may not be live. This may be IT enabling the paper based
recording process, which may not collect or analyse much data since the reports may
not demand such analysis. Reports may need to be generated at month end or for a
limited functionality e.g. Morbidity Report (fig 2.)
Fig 2. Morbidity report
(based on data collected from disparate sources and extrapolated as an example)
20. Level 2 : Patient data collection at the source using live applications and report
generation, where the electronically recorded data is used frequently for generating
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live reports for internal use of the organisation and organisation's management.
Fig 3. Patient Age Profile report
(based on data collected from disparate sources and extrapolated as an example)
21. Level 3 : Data analysis and decision support system to control spread of
diseases, where the data is communicated, with little or no latency, to other
organisations for collation and analysis. The analysis is used for taking decisions. In
properly designed processes, the analysis can provide real time scenarios for decision
makers.
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Fig 4. Disease Profile report for a locality
(based on data collected from disparate sources and extrapolated as an example)
22. Level 4 : Collection of geospatial / environment data and correlation with patient
data to link geospatial influences on diseases, where geospatial and other information
is used as part of analysis and decision making of Level 3.
23. For example, in the fig 5. an open drain can be seen next to the plot of
dysentery cases (orange) reported. This indicates that there may be some link
between open drain and dysentery cases, e.g. the sewerage water in the drain may
have contaminated the ground water being used by the residents of the area for
drinking etc.
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24. Similarly, a large number of Asthama cases (blue) can be seen along the road.
This may indicate that its a busy road, may be there is a lot of congestion and a lot of
pollution causing breathing trouble for residents. Cases around the road junction are
higher, indicating that junctions cause jams. Age profile analysis will be able to indicate
if its older people only (effect of age) or there is no distinction of age. There may also
be a correlation between severity during the day with high traffic movement time.
Fig 5. Geographical visualisation of reported cases
(based on data collected from disparate sources and extrapolated as an example to show correlation)
25. Since the data is available in electronic form, statistical analysis can be carried
out to discover patterns. A daily report of the kind above, can help visualise spread of
diseases. In fig 6 below, we can see that number of cases of Dysentery have
increased. We can also observe that low income group houses have a higher number
of cases. A daily plot of the dysentery cases can also be generated for daily changes
in the disease spread area.
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26. Such reports can help public health management officials with easy
understanding of data and, consequently, in taking decisions.
27. For example, for case of fig 5. Dysentery cases, public health officials could
request water and sewerage department to check the drain at places where cases
have been reported and are concentrated. Similarly, a local medical camp could be
setup to treat the patients. The camp would be adequately staffed, based on number of
cases expected, with doctors, paramedics, medicine and equipment to treat dysentery.
A local lab could also be setup for tests etc.
28. In doing this, the public health officials would reassign doctors, paramedics etc.
from hospitals which have low load and do not expect to use some of their equipment
during expected duration of treatment at the camp.
Fig 6. Geographical visualisation of reported cases
(based on data collected from disparate sources and extrapolated as an example to show correlation)
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29. For the cases of Asthma, the public health officials could ask traffic control to
reroute traffic to reduce congestion on the road under consideration, post traffic
policemen to manage traffic at the junctions. The officials could also ask the Public
Works Department to look at the condition of the road and to consider widening of road
or grade separation.
30. Level 5 : To use process of Level 4 to build models for spread of diseases and
take preemptive action for disease prevention.
(a) Building models : In the dysentery case, hypothetically, affecting parameters are
low income groups, paucity of piped water, low literacy, location proximity to drain,
low water table, damaged side walls or bottom of drain/over flow of drain etc. These
parameters could be used to search other localities prone to such outbreaks.
(b) Preemptive action : This could be planning for removal of the cause of diseases
e.g. strengthening / repair of drain walls and bottom, closing of drain, covering of
drain, providing piped water from municipality, asking residents to recharge the
water table by rain water harvesting, educating the residents about disease etc.
My little experiment with technology : Proof of Concept
31. To understand technology implementation and integration issues for this system,
I created a proof of concept environment to test the above postulations using open
source software.
32. I collected data from various healthcare providers spread over a geographical
area. Some of the data I could get was very minimal e.g. patient (without identity),
area, disease, gender. The data was recorded into OPD module of Care2x, a web
based Hospital Management System (HMS). In real implementation, Care2X (or similar
products) could be used as HMS and the data could be used in this experiment.
Care2x used PHP and MySQL running on Ubuntu Linux on a quad core AMD Phenom
PC.
33. The data was exported to R statistical package for statistical analysis.
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34. The data was also ported to Quantum GIS for plotting and geographical
analysis. I did not have vector files (amenable to electronic processing) of the
geographical area, so I converted some of the information into vector format.
35. I was able to successfully use a no cost system (other than existing hardware,
which was later re-utilised) to try out this experiment and generate basic reports. List
of open source softwares used for this project and their websites is at the end of this
paper.
Advantages
36. The numbers : The number of patients and types of diseases (Fig 4.) in a
locality can help in deciding in creating infrastructure of the healthcenter, its staffing
and medicine stock etc. If the data can be collected on daily basis or better, the
resource mobilisation can be optimal. Judicious use of manpower can improve quality
of service to the people and reduce cost of operations. Inventory/stock of medicines
can be appropriately maintained at the center to reduce wastage due to expiry and, if
need be, unused stock can be transferred to other centers.
37. Disease spread models can be exploited to reduce risk of spread of diseases.
A healthy population is more productive, more intelligent and contributes to generating
higher GDP.
38. Expenses : Planning and operations monitoring can reduce expenses. Data can
be used to design an optimal logistics system and carry out materials management
planning, to help reduce inventory of medicines/consumables and their subsequent
wastage due to expiry.
Conclusion
39. Usage of GIS based decision support system for managing public health can
greatly improve quality of health of the people in present times of diminishing
resources and rising expectations. Current state of IT readiness of the country and
availability of IT manpower is sufficient to start use of IT and GIS at a much larger
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scale and for newer applications than was possible a few years ago. It is possible to
use free (no cost) and open source softwares to achieve this aim.
40. This system can vastly reduce cost of operations and can help prevent
outbreaks of diseases, which are very common in India.
Use of open source (and free software)
41. As stated above, entire project was created using open source and free
software. Ubuntu and Fedora are the platform of choice which come pre-loaded with a
large number of free and open source software. Other softwares like R, Quantum GIS
were installed from the internet in a single click, using MTNL's internet connection.
Following are the softwares and their websites :
(a) Patient Records : OPD module of Care2X www.care2x.org. Uses MySQL database
www.mysql.com, PHP hypertext processor www.php.net and Apache webserver
www.apache.org (all are open source and free)
(b) Statistical Analysis : R Statistical Analysis package. (open source and free) www.r-
project.org
(c) GIS : Quantum GIS/GRASS.(open source and free) www.qgis.org / grass.fbk.eu
(d) Experiment platform : Ubuntu www.ubuntu.com / Fedora Linux
www.fedoraproject.org (open source and free)
(e) Image processing : The GNU Image Manipulation Program [The GIMP] (open
source and free) www.gimp.org
(f) Text processing and writing of the paper : LibreOffice (open source and free)
www.libreoffice.org
References
1. GIS and public health By Ellen K. Cromley, Sara McLafferty
4. Health and Environment Linkages Initiative – HELI
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5. National Cancer Institute Gegraphical Information System
6. Open source intelligence
7. www.uidai.gov.in
8. www.censusindia.gov.in
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Author :
Atul Asthana
Atul has over 27 years of experience in various capacities in government
and private sector. He was an officer with Indian Navy and managed
electronics, weapon systems and carried out R&D. He has been with
telecom and IT companies in roles of heading customer support,
marketing and business development. He has also been Principal
Consultant with Department of Information Technology, Government of
Delhi. He is an ICT, eGovernance and management consultant and is
presently with an international development agency. He has vast
experience of change management, process management / re-
engineering, project management and operations management. He can be
contacted at [email protected].
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