HEALTH INFORMATION SYSTEMS FOR DECISION MAKING

by Moses Lemayian

Health Informatics

Data for decision Making• Florence Nightingale invented polar-area diagrams in 1855 (below) to

show that many army deaths could be traced to unsanitary clinical practises and were therefore preventable. She used the diagrams to convince policy-makers to implement reforms that eventually reduced the number of deaths

Source: (Audain 2007). (Diagram from Nightingale 1858.)

Problem statement

• Information explosion: the amount of electronic data gathered is enormous In fact, some experts believe that medical breakthroughs have slowed down, attributing this to the prohibitive scale and complexity of present-day medical information. Computers and data mining are best-suited for this purpose. (Shillabeer and Roddick 2007).

data mining in the health sector• Early detection and/or prevention of diseases.

Cheng, et al (2006) cited the use of classification algorithms to help in the early detection of heart disease, a major public health concern all over the world.

• Cao et al (2008) described the use of data mining as a tool to aid in monitoring trends in the clinical trials of cancer vaccines. By using data mining and visualization, medical experts could find patterns and anomalies better than just looking at a set of tabulated data.

Sr_no Age N Small_n Percentage SE

1 15 – 24 10 3 32.2 16.2

2 25 – 34 19 6 29.9 11.5

3 35 – 44 35 23 64.3 8.8

4 45 – 54 77 62 81.3 4.8

5 55 – 64 99 90 90.8 2.6

Table 1. Drug

Sr_no Age N Small_n Percentage SE1 15 – 24 10 3 19.7 13

2 25 – 34 19 2 10.8 6.83 35 – 44 35 21 60.4 9.5

4 45 – 54 77 45 58.7 6.65 55 – 64 99 52 53 8.5

Table 2. Diet

Sr_no Age N Small_n Percentage SE

1 15 – 24 10 2 19.7 13

2 25 – 34 19 5 27 10.4

3 35 – 44 35 17 48.5 9.1

4 45 – 54 77 26 33.4 5.3

5 55 – 64 99 39 39.9 7.8

Table 3. WeightSr_no Age N Small_n Percentage SE

1 15 – 24 10 3 32.2 16.2

2 25 – 34 19 2 10.8 7.3

3 35 – 44 35 4 11.7 5.5

4 45 – 54 77 13 16.7 4.4

5 55 – 64 99 32 13.3 2.5

Table 4. Smoke cession

Sr_no Age N Small_n Percentage SE

1 15 – 24 10 3 32.2 16.2

2 25 – 34 19 6 33.3 10.2

3 35 – 44 35 13 36.9 7.9

4 45 – 54 77 23 29.9 5.6

5 55 – 64 99 28 27.9 5.4

Table 5. Exercise‘sr_no’ = serial number, (unique id - primary key), ‘age’ = age of patients, ‘N’ = total number of patient of each age group, ‘small_n’ = number of patients who have been cured with the particular type of treatment,percentage = percent of cured patients by specific mode of treatment, and ‘SE’ = Standard error.

Source: Abdulaziz et. al. (2010)Data: http://www.who.int/research/en/

Treatment p(Y) p(O) Comparison of p(O) with p(Y)

Drug –50.616 10.1015 P(O) > p(Y)

Diet 36.4803 65.8054 P(O) > p(Y)

Weight 32.1654 61.0199 P(O) > p(Y)

Smoke cession 12.9883 18.1215 P(O) > p(Y)

Exercise 48.5004 49.0474 P(O) = p(Y) {Approx equal}

Table 6. Comparison on predictions

CHALLENGESEven if data mining results are credible, convincing the health practitioners to change their habits based on

evidence may be a bigger problem. Ayres (2008)Shillabeer (2009) also reported most doctors (at least in Australia) prefer to listen to a respected opinion leader in the medical profession, rather than to the result of data

mining.

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