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The radical potential of open source programming in healthcare
May 21, 2015 | Nicholas Filler - Contributing writer
POSTED IN: Electronic Health Records, Analytics, Clinical
Everyone wants personalized healthcare. From the moment they enter their primary care clinic they have certain expectations that they want met in regards to their personalized medical care.
Most physicians are adopting a form of electronic healthcare, and patient records are being converted to a digital format. But electronic health records pose interesting problems related to sorting through vast amounts of patient data.
This is where open source programming languages come in, and they have the ability to radically change the medical landscape.
So why aren’t EHRs receiving the same care that patients expect from their doctor? There are a variety of answers, but primarily it comes down to how the software interprets certain types of data within each record. There are a variety of software languages designed to calculate and sort through large amounts of data that have been out for years, and one of the most prominent language is referred to as “R”.
What is R?
According to r-project.org “R is an integrated suite of software facilities for data manipulation, calculation, and graphical display.” Essentially this programming language has been built from the ground up to handle large statistical types of data.
Not only can R handle these large data sets, but it has the ability to be tailored to an individual patient or physician if needed. There are a variety of other languages focused on interpreting this type of data, but other languages don’t have the ability to handle it as well as R does.
How can a language like R change the way in which EHRs function?
Take, for instance, the recent debate regarding immunization registry. EHRs contain valuable patient data, including information associated with certain types of vaccine.
If you were able to cross reference every patient that had received a vaccine, and the side effects associated with said vaccine, then you could potentially sort out what caused the side effect and create prevention strategies to deter that certain scenario from happening again.
According to Victoria Wangia of the University of Cincinnati, “understanding factors that influence the use of an implemented public health information system such as an immunization registry is of great importance to those implementing the system and those interested in the positive impact of using the technology for positive public health outcomes.”
This type of system could radically change the way we categorize certain patient health information.
Programming languages like R have the ability to map areas that have been vaccinated versus those that haven’t. This would be ideal for parents who wish to send their children to a school where they know that “x” number of students have received a shot versus those that haven’t. Of course, these statistics would be anonymous, but this information might be critical for new parents who are looking for a school that fits their needs.
This technology could have much bigger implications pertaining to personalized data, specifically healthcare records. Ideally, an individual could tailor this programming language to focus on inconsistencies within patient records and find future illnesses that people are unaware of.
This has the potential to stop diseases from spreading, even before the patient is aware that they might have a life threatening illness. Although such an intervention wouldn’t necessarily stop a disease, it could be a great prevention tool that would categorize certain types of illness.
Benefits of open source
One of the more essential functions that R offers is the ability to be tailored to patient or doctor’s needs. Most information regarding patient health depends on how a physician documents the patient encounter, but R has the ability to sort through a wide variety of documentation pertaining to important statistical information that is relevant to physician needs. This is what makes open source programming languages ideal for the medical field.
One of the great components associated with open source programming languages in the medical field is the cost. R is a completely free language to start working in, and there is a large amount of great documentation available to start learning the language. The only associated cost would be paying a developer to set up, or create a program that quickly sorted through personalized information.
Essentially, if you were well rounded in this language, the only cost associated with adopting it would be the paper you would need to print information on.
Lastly, because of HIPAA, the importance of information security has been an issue, and should be a primary concern when looking at any sensitive electronic document. Cyber security is always going to be an uphill battle, and in the end if someone wants to get their hands on certain material, they probably will.
Data breaches have the ability to cost companies large amounts of money, and not even statistical data languages are safe from malicious intent. A recent issue has been the massive amount of resources that are being built in R that have been shared online. Although this is a step in the right direction for the language, people are uploading malicious code. But if you are on an encrypted machine, ideally the information stored on that machine is also encrypted. Cloud based systems like MySQL, a very secure open source server designed to evaluate data, offer great solutions to these types of problems.
These are some of the reasons why more physicians should adopt these types of languages, especially when dealing with EHRs. The benefits of implementing these types of systems will radically alter the way traditional medicine operates within the digital realm.
More statistical information about vaccinations and disease registries would greatly benefit those that are in need. The faster these types of systems are implemented, the more people we are able to help before their diseases becomes life threatening.
May 21, 2015 | Nicholas Filler - Contributing writer
POSTED IN: Electronic Health Records, Analytics, Clinical
Everyone wants personalized healthcare. From the moment they enter their primary care clinic they have certain expectations that they want met in regards to their personalized medical care.
Most physicians are adopting a form of electronic healthcare, and patient records are being converted to a digital format. But electronic health records pose interesting problems related to sorting through vast amounts of patient data.
This is where open source programming languages come in, and they have the ability to radically change the medical landscape.
So why aren’t EHRs receiving the same care that patients expect from their doctor? There are a variety of answers, but primarily it comes down to how the software interprets certain types of data within each record. There are a variety of software languages designed to calculate and sort through large amounts of data that have been out for years, and one of the most prominent language is referred to as “R”.
What is R?
According to r-project.org “R is an integrated suite of software facilities for data manipulation, calculation, and graphical display.” Essentially this programming language has been built from the ground up to handle large statistical types of data.
Not only can R handle these large data sets, but it has the ability to be tailored to an individual patient or physician if needed. There are a variety of other languages focused on interpreting this type of data, but other languages don’t have the ability to handle it as well as R does.
How can a language like R change the way in which EHRs function?
Take, for instance, the recent debate regarding immunization registry. EHRs contain valuable patient data, including information associated with certain types of vaccine.
If you were able to cross reference every patient that had received a vaccine, and the side effects associated with said vaccine, then you could potentially sort out what caused the side effect and create prevention strategies to deter that certain scenario from happening again.
According to Victoria Wangia of the University of Cincinnati, “understanding factors that influence the use of an implemented public health information system such as an immunization registry is of great importance to those implementing the system and those interested in the positive impact of using the technology for positive public health outcomes.”
This type of system could radically change the way we categorize certain patient health information.
Programming languages like R have the ability to map areas that have been vaccinated versus those that haven’t. This would be ideal for parents who wish to send their children to a school where they know that “x” number of students have received a shot versus those that haven’t. Of course, these statistics would be anonymous, but this information might be critical for new parents who are looking for a school that fits their needs.
This technology could have much bigger implications pertaining to personalized data, specifically healthcare records. Ideally, an individual could tailor this programming language to focus on inconsistencies within patient records and find future illnesses that people are unaware of.
This has the potential to stop diseases from spreading, even before the patient is aware that they might have a life threatening illness. Although such an intervention wouldn’t necessarily stop a disease, it could be a great prevention tool that would categorize certain types of illness.
Benefits of open source
One of the more essential functions that R offers is the ability to be tailored to patient or doctor’s needs. Most information regarding patient health depends on how a physician documents the patient encounter, but R has the ability to sort through a wide variety of documentation pertaining to important statistical information that is relevant to physician needs. This is what makes open source programming languages ideal for the medical field.
One of the great components associated with open source programming languages in the medical field is the cost. R is a completely free language to start working in, and there is a large amount of great documentation available to start learning the language. The only associated cost would be paying a developer to set up, or create a program that quickly sorted through personalized information.
Essentially, if you were well rounded in this language, the only cost associated with adopting it would be the paper you would need to print information on.
Lastly, because of HIPAA, the importance of information security has been an issue, and should be a primary concern when looking at any sensitive electronic document. Cyber security is always going to be an uphill battle, and in the end if someone wants to get their hands on certain material, they probably will.
Data breaches have the ability to cost companies large amounts of money, and not even statistical data languages are safe from malicious intent. A recent issue has been the massive amount of resources that are being built in R that have been shared online. Although this is a step in the right direction for the language, people are uploading malicious code. But if you are on an encrypted machine, ideally the information stored on that machine is also encrypted. Cloud based systems like MySQL, a very secure open source server designed to evaluate data, offer great solutions to these types of problems.
These are some of the reasons why more physicians should adopt these types of languages, especially when dealing with EHRs. The benefits of implementing these types of systems will radically alter the way traditional medicine operates within the digital realm.
More statistical information about vaccinations and disease registries would greatly benefit those that are in need. The faster these types of systems are implemented, the more people we are able to help before their diseases becomes life threatening.
