GEOGRAPHIC INFORMATION SYSTEMS APPLIED TO

PATIENT DISTRIBUTION FOR FAMILY HEALTH TEAMS

IN PRIMARY HEALTH CARE

João Azevedo

, Carlos Oliveira

, Juliano Gaspar

1,2

and Alberto Freitas

1,2

CIDES – Department of Health Information and Decision Sciences, Porto, Portugal

CINTESIS – Center for Research in Health Technologies and Information Systems

Faculty of Medicine, University of Porto, Porto, Portugal

Keywords: Geographic information systems (GIS), Public health, Community health, Nursing, Family nursing, Primary

care physicians.

Abstract: Background: Family focused environment in Primary care is considered to be the future and help is required

to implement new conceptualities. One theory consists in dividing patients accordingly to geographic

clusters.

Aim: To study and implement methodologies for distribution of patients of a health unit, and develop a tool

to aid in this process.

Methods: A health unit was selected to recollect and process bio-geographic data of patients. A manual

division was executed and various methods were tested. An information system was developed in order to

help divide and compare between manual and automatic.

Results: The original data contained a significant percentage of errors (25%). This led to the cross validation

of addresses. This process took months. Only after, various patient division techniques could be applied.

One showed itself as having the most advantages. A robust GIS system was developed.

Discussion: The analysis took a significant amount of time. The method of dividing the patients proved

itself appropriate to this situation, and could probably be applied in many urban locations. The obtained GIS

provided time saving and better data comprehension.

Conclusion: Technologies in general and the system developed in particular can help patient allocation and

represent a breakthrough in time-saving.

1 BACKGROUND

1.1 Primary Health Care

Primary Health Care is a key element in any Health

System. It is in the front line, being the first contact

with the population. Health promotion, self-

empowerment and disease prevention are the pillars

to a successful Primary System (Atun, 2004).

Studies show, that in countries where an

emphasis is given to Primary Care, global health

costs are diminished (Atun, 2004). Home Care is

prioritized and Professionals are encouraged to have

a pro-active behavior and seek out the patient in his

home environment.

Recently, a new paradigm on health is trying to

be introduced, the methodology of allocate a

“Family Health Team” to every patient (MS, 2007).

It consists on a Physician and a Nurse. Also, one of

the main theories nowadays talks about clustering

patients in geographic areas, meaning, dividing

them accordingly to their home address. These

theories come, mainly, from the nursing area but are

extensible to both areas (OE, 2002); (MS, 2007);

(OE, 2007); (Joel and Stallknecht, 2000).

1.2 Geographic Information Systems

Geography takes a fundamental role in almost all

decision we made. A GIS can be defined by: “a

computer-based system for integrating and

analyzing spatially referenced data”(Cromley,

2003).

419

Azevedo J., Oliveira C., Gaspar J. and Freitas A..

GEOGRAPHIC INFORMATION SYSTEMS APPLIED TO PATIENT DISTRIBUTION FOR FAMILY HEALTH TEAMS IN PRIMARY HEALTH CARE.

DOI: 10.5220/0003791104190422

In Proceedings of the International Conference on Health Informatics (HEALTHINF-2012), pages 419-422

ISBN: 978-989-8425-88-1

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

It is proven that the use of GIS in health care is

effective (Graves, 2008) and useful in discovering

new patterns and optimize existing resources.

As quoted by Dredger S: “if a picture is worth a

1000 words, then a map is worth 1000 pictures”

(Dredger et al., 2007).

1.3 Motivation

This study results from a request made by the

Clinical Council of “Agrupamento de Centros de

Saúde Porto Ocidental (ACES)” and the execution

of a master thesis in the same subject.

2 AIM

The aim of this study is to analyze and implement a

distribution of patients of a health unit, and to

develop a tool that can aid in this process. The

distribution should be calculated throw various

tested rules, using data from a health organization.

3 METHODS

This study followed this methodology: Select a

health unit; compile biographic and geographic

data; process it in order to filter errors; execute a

manual division of patient; test various methods to

divide; develop an information system; present the

results to the health unit; compare manual to

automatic division.

3.1 Family Health Unit Data

This study was made in Portugal, in the city of

Porto, in the western Health Center (Agrupamento

de Centros de Saúde Porto Ocidental”) in order to

help opening a new Family Health. For this unit, 5

lists of patients were needed because there were 5

Physicians and 5 Nurses. The data included 3

existing units as seen in table 1.

Table 1: Number of patients existing in original units.

Health Units

Existing

patients (n)

Unit X Unit Y Unit Z

5439 7252 2021

Mixing all 3 databases resulted in a total of 4714

patients. From these, only the patients that lived in

Porto city where selected n = 12848; after that a

new selection was made excluding all patients that

lived outside the Health Unit influence area. This

area comprehended 4 parishes within Porto city. .In

this area we get a total of 8421 patients (table 2).

Data was analyzed with the use of statistical

measures and cross validation with central post

databases of the country.

3.2 Distribution Criterions

The main methods that were used focused on

dividing the population having in consideration

their distance to the central location (the health

unit), the discrepancy between different lists, the

minimum weighing number of people to exist

within a list (1917 to 2412) and the real number of

existing people acceptable by professionals (1550 to

1800 - Division criterions are stated in table 2.

The division of patients was made by: Manual

Division; Matrix Grid division; Circular cluster

division; Concentric circles and Triangular

expansive out method.

Table 2: Existing patients in used database and their

weighting.

Age interval

Average

weight

Weighting

formula

Weighted

[0 to 6[ 1,5 347 n * 1,5 520,5

[6 to 65[ 1 6590 n 6590

[65 to 75[ 2 678 n * 2 1356

[>= 75] 2,5 806 n * 2,5 2015

Total 8421 10481,5

4 RESULTS

4.1 Data Analysis Results

The final selected amount of patients was as

showed in table 2. Patients were divided using the

criterions mentioned earlier.

Table 3: Number and percentage of errors found in

database.

Error description n %

Missing door number 511 6,07

Missing City 13 0,15

Missing street 25 0,30

Address incorrect (street name non

existing)

2137 25,38

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Many errors were encountered within the

database as shown in Table 3. These had to be

addressed one by one in order to correctly input the

coordinate in the map. This validation took months.

4.2 Patient Distribution Method

A variety of methods were used in order to divide

the 8421 patients between 5 different groups.

The most common method used by Health Units

is the manual one. Health professionals use printed

maps and clipboards in order to distribute people.

This method was used in order to primary divide

people and lasted 9 months. Another method tested

is systematized and implies a matrix division (grid

display) of lists (shown in 1

capture of figure 1).

Figure 1: Representation of patient division methods

used.

In Circular Cluster Division, circular clusters

were drawn not having the Health unit as center

(seen in 2

capture of figure 1).

Using Concentric Circles, the unit was placed in

the center of the map and concentric circles are

drawn, from the inside to outside as seen in 3

capture of figure 1. Finally we have the Triangular

Expansive out method, in which we place the

Health unit in the center and then lines are drawn

similar to a circle division in slices (shown in 4

capture of figure 1).

4.3 GeoPrimaryHealth

The system obtained is a GIS destined to Health. It

used HTML, CSS, Ajax, PHP and Javascript. The

server has Apache and MySQL database manager.

There is also use of Google Maps API 3.0.

Non-systematized division of the patients can be

seen in fig. 2. In figure 3 we can see a glimpse of

the patient distribution made using the method

earlier chosen.

Figure 2: System showing the initial patient distribution.

5 DISCUSSION

5.1 Data Errors

It is vital to centralize the raw data of all population

for health databases feeding. Addresses, phone

numbers, birthdates are a few of fields that have to

be validated and centralized in order to avoid

patient process duplication or misinformation.

Figure 3: Representation of patient division methods

used.

5.2 Division Method Chosen

The method that had the most benefits was the

triangular expansive out because: with this

distribution there’s always a non-significant

difference in terms of the distance between the

patients and the central unit. Due to the fact that

there’s no perfect geometric division in cities, the

method choose in the system was called: “Street

based semi-automatic triangular expansive out”.

5.3 Problems in the Division Process

One of the most important problems was the fact

GEOGRAPHIC INFORMATION SYSTEMS APPLIED TO PATIENT DISTRIBUTION FOR FAMILY HEALTH

TEAMS IN PRIMARY HEALTH CARE

421

that: if we really allocate cluster geographically,

what will we do with the following future

migrations within or without the major primary

cluster. It’s expected that families that today live in

one cluster may move to another or outside the

major one. Moving outside all clusters can be easily

addressed, but moving within different cluster can

be difficult.

Reality and legal environment shows that

moving people from one physician to another is

hard due to the lack of professionals. Also, in the

beginning we are geographically dividing clusters

of people in the assumption that it’s better for work

practice and for patient health. Changing the rules

in the future destroys all of the primary purposes.

Another very common problem can be the

constant cycle of in and out of health professionals

(hiring, retiring, etc.). If we make a geographical

cluster with all of its population, but in the same

area there are other patients that have different

Family Health Teams. In the beginning they’re not

considered to enter the cluster, but they actually live

within its area. If the Team of the second groups

moves away, a group of people emerges within an

already full formed cluster. What to do? Have they

not the same right as the other to belong in an

existing formed cluster? Does the team have to

enlarge its number limits and endanger healthcare

quality? Have we the right to destroy a full formed

and functional cluster? These are all questions

rather difficult to answer.

5.4 GeoPrimaryHealth Suitability

Taking in consideration that the manual method

used took 6 months and that with the help of the

system developed time spent was only hours, its’

suitability and advantage is significant. Of course,

this is only due to the fact that polluted data was no

longer present and a correct coordinate could be

extracted without a doubt from patient addresses.

This means that is still much to do in order to clean

databases and obtain a reliable source of

information.

6 CONCLUSIONS

After the completion of this work, we can conclude

that the system developed, can help patient

allocation and that represents a breakthrough in

time-saving. Doing this automatically after the

system is fully developed, took 1 hour in opposition

to months doing it manually.

6.1 Future Work

One of the future work that can be developed is to

follow the Health unit that adopted this distribution

of Patients in order to identify what changes

occurred. Finally, we can also distribute this system

between all Health Units that may need it in order

to verify if this method of distribute patients is

usable in other scenarios (small villages, rural

environments, islands, etc.).

ACKNOWLEDGEMENTS

To the “ACES Porto Ocidental” for requesting our

help and all the collaboration given. To the unit that

was created for all the help and patience.

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