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Data Analytics: Redefining Healthcare

The preservation of life in a population depends on proper decision-making, and disease control bodies have in recent times leveraged data in favor of humanity.

Data from past health woes has been of strategic importance, informing healthcare professionals on crucial insights that have been used to make data-driven, life-saving decisions.

Data analytics is the use of processes and technological tools to expose insights from data.

Let us examine four types of data analytics and their benefits to healthcare:

  • Descriptive analytics: This is aimed at examining past events and occurrences. For example, the number of affected individuals in a population can indicate how contagious a virus is.
  • Diagnostic analytics is the process of determining why something happened by identifying patterns, and it can be used to determine the causes of medical conditions.
  • Predictive analytics: It provides forecasts about what could happen in the future. It can be used to forecast the spread of a virus by using the data of other viruses from previous years.
  • Prescriptive analytics: It details what measures to take to eradicate or avoid negative outcomes. A use case could be to access pre-existing outbreaks and suggest or implement preventive measures for tackling them if they do occur in the future.

Historic data provides a base for which insights can be derived. Data to watch out for may include:

  • Demographic & Socio-economic data: This can help shed light on the prevailing risk behaviors in a population. Several places collect population-based food preference questionnaires, which are important in informing how food consumed can relate to foodborne outbreaks. A study on malnutrition in a rural community can reveal that malnutrition is linked to poor household incomes of the parents. Such information can be pivotal in the assessment of a disease or illness and its likelihood of being caused by malnutrition.
  • Electronic health records: Electronic health records are an interesting source of data, as hospitals are the main frontliners in administering health. These records include age, gender, lab results, profession, medical history, etc. Another is data from the use of the internet through social media, which can be harnessed to tell what is happening, where, and why.
  • Laboratory data: It may be used in determining if a cluster of cases are linked or caused by random events.

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