Case Study 2017

Electronic health records & bioinformatics

This sections looks at data included in electronic health records, applications of bioinformatics and predictive medicine.

Helpful Reads:

  • “What is an electronic health record?” [1]

Electronic health record (EHR)

An electronic health record (EHR) is a digital version of a patients health information, that aims at providing up-to-date information instantly and securely. Information can include:

  • medical history
  • diagnoses
  • medications
  • immunization dates
  • treatment plans
  • allergies
  • radiology images
  • laboratory & test results

This information can help clinicians to make better decisions or to improve their workflow. [1]

Difference EMR & EHR

An electronic medical record (EMR) contains the standard medical health information that would normally be recorded on paper by health care providers. An EHR on the other hand has the additional purpose of being more universal, with the possibility of being shared between different health care providers.



  • tracks data over time
  • identify patients due to medical treatment
  • improved health care through accurate and complete health information
  • prevent potentially risky medications or tests
  • easy to share medical information between different health care providers or institutions


  • privacy and security concerns on EHR data storage
  • threats to information: hackers, natural disasters, technology failures
  • might actually decrease workflow efficiency
  • documentation errors
  • compatibility between different health service providers

A possibility currently being investigated is to use blockchain technology (as in Bitcoin) to ensure privacy, security and integrity of EHRs.


Definition from the Nature website[2]:

“Bioinformatics is a field of study that uses computation to extract knowledge from biological data. It includes the collection, storage, retrieval, manipulation and modelling of data for analysis, visualization or prediction through the development of algorithms and software.”

The primary goal of bioinformatics is to gain a better understanding of how biological processes work. Some example applications include (Wikipedia):

  • sequence analysis: gaining an understanding of different genes in DNA
  • gene and protein expression: analysis of RNA to determine which genes are actually being expressed
  • protein location: helps to understand the role of a specific protein
  • protein structures: further understanding of the function of proteins
  • network analysis: understand the relationships of biological networks, e.g. metabolism or protein interactions

Found information is often compiled in databases available for research.

Genomic bioinformatics

Also computational genomics, this is a subfield of bioinformatics dedicated to researching genomics. The definition for genomics from the Nature website [3]:

“Genomics is the study of the full genetic complement of an organism (the genome). It employs recombinant DNA, DNA sequencing methods, and bioinformatics to sequence, assemble, and analyse the structure and function of genomes.”

The bioinformatic aspect of this field includes:

  • DNA sequencing
  • Sequence assembly
  • Genome annotation
  • Tracing evolution of DNA
  • Genetics of disease
  • Analysis of mutations in cancer

Predictive diagnosis / Predictive medicine

Definition of predictive medicine from the Nature website[4]:

“Predictive medicine is a branch of medicine that aims to identify patients at risk of developing a disease, thereby enabling either prevention or early treatment of that disease. Either single or more commonly multiple analyses are used to identify markers of future disposition to a disease.”

Examples of predictive medicine:

  • diagnostic testing, e.g. blood tests, urine test, in order to confirm some diagnosis
  • newborn screening
  • predictive risk testing through genetics
  • prenatal testing for couples with increased risk of genetic diseases/conditions


  • genetics are not the only factor for diseases and external factors might be hard to take into account
  • reducing negative environmental factors such as smoking
  • potentially negative psychological effects of knowing to have a higher risk for certain disease


[1] “What is a electronic health record?”

[2] “Bioinformatics”

[3] “Genomics”

[4] “Predictive Medicine”