Life expectancy has increased in both developed and developing countries since the end of the 19th century and significantly since 2001. The major progress has been in reducing child mortality.
Longer life should not be synonymous with increased disability and reduced quality of life. Physicians have identified activities that are necessary to maintain quality of life. Medicine now has two goals: to care for people and to keep them healthy.
Care with Machine Learning
Medecine is a continuous and iterative process consisting of three steps: Diagnosis, Prognostic, Care. The Machine Learning can intervene in these three stages.
- Diagnosis: What is wrong with me?
- Prognostic : What will happen to me if I don’t treat myself?
- Care: What do I do to take care of myself? What medications am I prescribed? It is necessary for the physician to continually reinterpret the results of care on his patients.
Keeping people healthy
To keep people healthy, three actions are carried out: monitoring the prevalence of diseases, understanding virus contamination, and quarantine. Each of these actions can benefit from data analysis and Machine Learning.
- Monitoring disease prevalence: monitoring the number of cases of a disease in a population at a given time, including both new and old cases. The World Health Organization is now in charge of ICD-10 and ICD-11: a list of medical classification codes for diagnoses and procedures.
- Understand infections and viruses mode of contamination. In the context of the Covid-19 epidemic, the analysis of the data allowed to understand very quickly the factors of transmission.
“From January 14 to February 12, 2020, the Shenzhen Center for Disease Control and Prevention identified 391 cases of SARS-CoV-2 and 1286 close contacts. We estimated the parameters of disease transmission and analyzed the factors influencing the risk of transmission.”
- Quarantining populations is a solution to limit the transmission of contagious diseases. It was practiced before Coronavirus to fight against yellow fever, cholera, diphtheria, tuberculosis, plague, smallpox, Ebola, AIDS. During the COVID-19 crisis, in order to decide on quarantine, some countries such as China made available applications based on Artificial Intelligence.
New technological and human investments
There are new data collection needs that are emerging along with new disciplines of care. Quantified self, genetics, metagenomics are burgeoning disciplines that require rethinking the practice of medicine and the education of caregivers.
- Need to implement Big Data architecture and invest in IT to collect socio-demographic, quantified self data and link it to patient medical data. In the USA: 1 to 2% of healthcare spending is allocated to IT compared to 6 to 7% in other sectors and 10 to 12% in banking.
- Need to exploit the data generated by the expansion of new care disciplines: genetics, metagenomics. With the development of new technologies, biomedical science has been transformed into a digitized science generating data.
- Need to put in place training and incentive policies for caregivers, from researchers to nurses, to encourage them to use the data and the Machine Learning.
Explosion of Healthcare costs
The aging of the population with a guaranteed quality of life, the emergence of personalized medicine based on Big Data and machine learning, the new innovative treatments made possible by the rise of precision medicine: this new paradigm of medicine has a very high cost.
In order to finance these innovations, the growing expectations of patients : healthcare spending has soared, especially in developed countries. This expenditure does not always have the desired effect. The USA, the OECD country with the highest percentage of GDP invested in health care, has a lower life expectancy than countries that spend much less.
Many unnecessary expenses, wastage, overmedication or prescription errors are at the root of the cost explosion. Machine Learning and data have a role to play in increasing the productivity and efficiency of healthcare systems around the world.
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