2. HEALTH INFORMATION SYSTEMS FOR DECISION MAKING by Moses Lemayian Health Informatics

3. 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.)

4. 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).

5. 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.

6. Sr_noAgeNSmall_nPercentageSE 115 – 2410332.216.2 225 – 3419629.911.5 335 – 44352364.38.8 445 – 54776281.34.8 555 – 64999090.82.6 Table 1. Drug Sr_noAgeNSmall_nPercentageSE 115 – 2410319.713 225 – 3419210.86.8 335 – 44352160.49.5 445 – 54774558.76.6 555 – 649952538.5 Table 2. Diet Sr_noAgeNSmall_nPercentageSE 115 – 2410219.713 225 – 341952710.4 335 – 44351748.59.1 445 – 54772633.45.3 555 – 64993939.97.8 Table 3. Weight Sr_noAgeNSmall_nPercentageSE 115 – 2410332.216.2 225 – 3419210.87.3 335 – 4435411.75.5 445 – 54771316.74.4 555 – 64993213.32.5 Table 4. Smoke cession Sr_noAgeNSmall_nPercentageSE 115 – 2410332.216.2 225 – 3419633.310.2 335 – 44351336.97.9 445 – 54772329.95.6 555 – 64992827.95.4 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/

7. Treatmentp(Y)p(O) Comparison of p(O) with p(Y) Drug–50.61610.1015 P(O) > p(Y) Diet36.480365.8054 P(O) > p(Y) Weight32.165461.0199 P(O) > p(Y) Smoke cession12.988318.1215 P(O) > p(Y) Exercise48.500449.0474 P(O) = p(Y) {Approx equal} Table 6. Comparison on predictions CHALLENGES Even 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.

8. END THANK YOU