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Predictive Analytics, Healthcare and a Life much awaited

Predictive Analytics, Healthcare and a Life much awaited


Predictive Analytics, Healthcare and a Life much awaited

Jyotika is 61 years old and lives in a small town in Lucknow and is suffering from Diabetes for the past 14 years. She is coping well with fair HB1AC values and lifestyle management. Recently, Jyotika felt that she is not able to handle sunlight well and feels that her vision is a bit blurred- she is speculating cataract and shows up at Dr. Narula’s Clinic. Dr. Narula is the Chief Ophthalmologist and has more than 35 years of experience and currently runs a Polyclinic out of Lucknow, UP.



healthcareJyotika gets successfully operated for Cataract and is discharged the same day. Within 13 days of discharge, Jyotika is admitted again but this time because of a Cardiac Disease. The family is speculating if this has something to do with the recent cataract surgery. Dr Reddy, Head of Department, Cardiology shows up and makes a comment to the family and Nurse- Well! Oxidative damage to proteins in the human lens is believed to be important in the etiology of age-related cataract. Because free radical-mediated oxidative damage to lipoproteins may accelerate atherosclerosis, the authors hypothesized that the development of cataract might be a marker for such damage and therefore might be associated with future risk of coronary heart disease (CHD).

Mahesh, husband of Jyotika is not very happy with this. He believes that the doctors demonstrated clear negligence when Jyotika was admitted last time and provided no warning signs related to any possibility of a heart disease.

Is there a way that this story would have been different and more assuring for the patient?

Predictors like Physiological data (systolic blood pressure); Predictive Drugs usage and context (drug use of alpha blockers, beta blockers, beta agonists), Previous disease history (especially COPD, Lifestyle and environmental factors (age, smoking, and other coronary risk factors) can help determine risk for the population well in advance with Predictive analytics in action.

Quick stats- In the United States, 1 in 4 women dies from heart disease. In fact, coronary heart disease (CHD)—the most common type of heart disease—is the #1 killer of both men and women in the United States. Also, a greater proportion of women (52 percent) than men (42 percent) with myocardial infarction die of sudden cardiac death before reaching the hospital.

enlightiks-300x300Advanced Predictive analytics companies like Enlightiks (based in India, US) have developed interesting risk stratification modules that can crawl of millions of health records in a population to understand patterns, trends, patient similarity metrics and natural language processing system that also takes into context unstructured data like Physician notes and discharge documents that are not often taken into account for analysis.

Data goes through normalisation in a smart grid atmosphere and is then put into context to further do analysis based on simple algorithms like Framingham’s scale coupled with patient similarity study, isomorphism of patient graphs in a population, building cohorts and then aligning the data to the outcomes. This provides more complete and accurate understanding of each patient.

While studying the Prospective study of cataract extraction and risk of coronary heart disease in women 60,657 women aged 45–63 years were followed. These women were without known coronary disease, stroke, or cancer in 1984.During 10 years of follow-up (674,283 person-years), the authors of this report (see source) documented 887 incident cases of CHD and 2,322 deaths. After adjustment for age, smoking, and other coronary risk factors, cataract extraction was significantly associated with higher risk of CHD (relative risk (RR) = 1.88, 95% confidence interval (CI): 1.41, 2.50) for total CHD, 2.44 (95% CI: 1.54, 3.89) for fatal CHD, and 1.63 (95% CI: 1.14, 2.34) for nonfatal myocardial infarction). The positive association between cataract extraction and total CHD was stronger among women with a history of diabetes (RR = 2.80, 95% CI: 1.77, 4.42) than among those without reported diabetes (RR = 1.51, 95 percent CI: 1.04, 2.18). In multivariate analyses, cataract extraction was associated with significantly increased overall mortality (RR = 1.37, 95 percent CI: 1.13, 1.66), which was entirely explained by the increased mortality from cardiovascular disease (RR = 1.84, 95% CI: 1.29, 2.64). These findings are compatible with current hypotheses relating oxidative damage and tissue aging to the development of cataract and CHD.
healthcareData mining and data analytics has been of immense importance to many different fields as we witness the evolution of data sciences over recent years. Biostatistics and Medical Informatics has proved to be the foundation of many modern biological theories and analysis techniques. These are the fields which applies data mining practices along with statistical models to discover hidden trends from data that comprises of biological experiments or procedures on different entities.

In case of Jyotika if risk stratification was possible, it would have allowed advanced preventative measures and would have also help bring down healthcare costs especially for a country like India where 80% of healthcare expenditure is out of pocket. The claims and hospital admission would have been carefully planned or avoidable. Also, such measures would have led to better patient satisfaction.

Currently at Enlightiks we are working on Predicting the unknown. We have presented several papers including the recent one on “Predicting Risk of Diabetes in Non-Diabetic Population”. Sometimes Indian healthcare providers do shy away from such advanced analytics and stick to basic Business intelligence tools. We have encouraging examples like Aarogyasri Health Care Trust, Mitra Biotech (Cancer research) as Indian case studies as to how to utilise data to build insights.

Using healthcare data smart, securely and privacy-safe, it can bring a big boost to any healthcare system helping provide best quality, personalized healthcare at a much affordable cost.


Credits:

Cardiac Data Mining (CDM); Organization and Predictive Analytics on Biomedical (Cardiac) Data. Musa M Bilal, Masood Hussain, Iqra Basharat, Mamuna Fatima, AIP Conference Proceedings 01/2013; 1559. DOI: 10.1063/1.4825018

See Also

A Predictive Model for Readmission of Patients with Congestive Heart Failure: A Multi-hospital Perspective- Eric Zheng

Prospective study of cataract extraction and risk of coronary heart disease in women. Hu FB1, Hankinson SE, Stampfer MJ, Manson JE, Colditz GA, Speizer FE, Hennekens CH, Willett WC.

nhlbi.nih.gov, How Does Heart Disease Affect Women?

http://www.cdc.gov/media/dpk/2013/images/vitalSigns/heart_disease/img14.jpg

Recent Insights in Coronary Artery Disease in Women- Biswakes Majumder, Azizul Haque, Dipankar Ghosh Dastidar

Prevalence of risk factors for coronary artery disease in an urban Indian population- 2014- T Sekhri, R S Kanwar, R Wilfred, P Chugh, M Chhillar, R Aggarwal, Y K Sharma, J Sethi, J Sundriyal, K Bhadra, S Singh, N Rautela, Tek Chand, M Singh, S K Singh

Prevalence of coronary artery disease and coronary risk factors in Kerala, South India: A population survey – Design and methods- Geevar Zachariah,a S. Harikrishnan,b M.N. Krishnan,g,∗ P.P. Mohanan,c G. Sanjay,d K. Venugopal,e K.R. Thankappan,f and The Cardiological Society of India Kerala Chapter Coronary Artery Disease and Its Risk Factors Prevalence (CSI Kerala CRP) Study Investigators


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