Public Health Management-An Analytical Approach Using GIS Based Knowledge Management-Atul Asthana

8/3/2019 Public Health Management-An Analytical Approach Using GIS Based Knowledge Management-Atul Asthana

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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

This paper was submitted for eINDIA 2011 conference Page 1



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




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




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




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




live reports for internal use of the organisation and organisation's management.

Fig 3. Patient Age Profile report


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.




Fig 4. Disease Profile report for a locality


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.




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.




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)




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.




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




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


http://www.care2x.org/

http://www.mysql.com/

http://www.php.net/

http://www.apache.org/

http://www.r-project.org/


http://www.qgis.org/

http://grass.fbk.eu/

http://www.ubuntu.com/

http://www.fedoraproject.org/

http://www.gimp.org/

http://www.libreoffice.org/

http://www.who.int/entity/heli/en/

http://www.care2x.org/

http://www.mysql.com/

http://www.php.net/

http://www.apache.org/



http://www.qgis.org/

http://grass.fbk.eu/

http://www.ubuntu.com/

http://www.fedoraproject.org/

http://www.gimp.org/

http://www.libreoffice.org/

http://www.who.int/entity/heli/en/



5. National Cancer Institute Gegraphical Information System

6. Open source intelligence

7. www.uidai.gov.in

8. www.censusindia.gov.in


http://gis.cancer.gov/examples/general.html

http://en.wikipedia.org/wiki/Open-source_intelligence

http://www.uidai.gov.in/

http://www.censusindia.gov.in/

http://gis.cancer.gov/examples/general.html

http://en.wikipedia.org/wiki/Open-source_intelligence

http://www.uidai.gov.in/

http://www.censusindia.gov.in/



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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Public Health Management-An Analytical Approach Using GIS Based Knowledge Management-Atul Asthana