Let’s talk about big data and the role it plays in the healthcare industry.
Since the dawn of the internet age, the sheer amount of information that we’re exposed to each day is overwhelming. From social media posts with targeted ads to forums, chat rooms and YouTube videos, to name but a few, the amount of data we are personally bombarded with is almost impossible for us to process successfully.
And this goes doubly so for industry, where the amount of data can be ten-fold. But within this flood of data there is undoubtedly useful insights to be gleaned on everything from customer spending habits to the success of academic research. And it’s not just private businesses that can profit from this data.
In this article, we’ll concentrate on big data in the healthcare industry and show you how it can be used in various areas such as medical simulation to improve patient care and performance.
So, let’s get started…
4.1 Clinical Trials
8.2 Power BI
Ok, if you’re reading this, you probably already know what the term big data means, but, just in case, let’s do a quick review.
Put simply, “big data'' is a catch-all term for the accumulation of the massive amounts of information generated by businesses or organisations on a daily basis. This data can come from a diverse range of sources such as social media interactions, surveys and interviews, internal or external databases, new technologies, such as Codimg, which have been designed to collect data for analysis, or any number of alternative sources.
So, what happens to all this data? In some organisations, especially those who haven’t yet harnessed the power of big data, it is either collected in a rudimentary form and left to moulder in a harddrive at the back of the equipment cupboard, or, worse still, it simply disappears into the ether, never to be thought about or considered ever again.
But allowing this to happen would be a huge mistake.
You see, big data is a supremely useful resource for modern businesses. When handled correctly, big data can provide insights and make predictions on where an organisation is headed, revealing its strengths and weaknesses. In turn, this allows company leaders to create plans which, when implemented, help steer the organisation, making working processes more effective and, in the long run, more profitable.
But big data isn’t easy to work with due to the sheer volume that is produced. And that’s where dedicated analysis software such as Codimg, which can be used to both collect and analyse data, or business visualisation software like Power BI, Python and Tableau come into play.
We’ll talk about these later, but first let’s look at the healthcare industry.
In terms of healthcare which, in general (and outside of the USA), is more concerned about patients than profit, this can translate to improved patient safety, reduced medical error and, ultimately, lower mortality rates.
The analysis of big data can also be applied to medical research and investigation, improving working and training processes in environments such as the simulation lab and basically raising hospital standards in all areas.
Here are a few areas where big data analysis can be applied in a hospital setting.
Medical simulation laboratories are often at the cutting edge of healthcare research and development. As well as providing an essential teaching environment for doctors and nurses, in these safe environments, researchers can investigate, quantify, and ultimately evolve modern healthcare practices to improve patient care.
Some of the areas in which medical simulation might be applied include:
Obviously, in medical simulation, huge reams of data are created and simulation practitioners need reliable solutions to collect, analyse and use this data. We’ve already talked at length about how Codimg can be used as an analytical tool in the simulation lab. Please feel free to check out this article about how Codimg is used at the University Hospital of the Canary Islands and this article on its use for Objective Structured Clinical Exam assessment.
In addition, check out this video from simulation expert Esther León of the Universitat of Barcelona:
Obviously, one of the fields in which big data can be utilised is that of academic research, and there are many areas of research in healthcare. Let’s take a look at a few…
A clinical trial is the study of the effects of new drugs, methods of surgery, or medical technology on a hand-picked group of volunteers. This is an extremely important phase in getting new treatments approved by government regulators and onto the market and, as such, they must follow stringent, established research rules.
These trials produce huge amounts of data which must be thoroughly analysed in order to make headway in the process. Please check out this article on cohort interviews, where we discuss one of the most important aspects of clinical trials, the volunteers themselves, and show you how their testimony can be turned into objective, usable data.
Related to psychology and psychiatry, this type of research aims to discover how people react in different situations. Examples of this might be a study on how stress affects a person’s judgement, how lack of sleep can affect motor control, or how excessive caffeine consumption affects the thinking process.
For the most part, we’re talking about the study of observable behaviour here and, as with clinical trials, there is a massive amount of data that can be produced. Obviously, when studying behaviour, it’s important that the observer remains completely neutral and doesn’t try to influence the volunteer in any way. It’s also important that the generated data is 100% objective and unsullied by personal opinion or views.
One of the best ways of obtaining this type of data is with the use of a Gesell Chamber observation room. Please check out this article about setting up a Gesell Chamber and also this case study about a forensic psychology project that Codimg was involved in at the Court of Justice in the Canary Islands, and check out this interview:
This is probably the most wide-ranging type of research which might encompass many areas of healthcare.
The purpose of public health research is to to understand the varying factors that determine the general health of the population. This can include aspects such as government policy, socioeconomic factors, and levels of education. From here, researchers can suggest interventions and changes to policy to improve the general well-being of the entire population.
Obviously, this type of research is big data personified with reams of data coming from multiple sources. In this case, comprehensive analysis tools like Codimg are absolutely essential to the process.
As an example of this type of research, check out this article on the rights of medication administration which were developed as a mean to reduce medical error and preserve the rights of the general population.
There are many other types of healthcare, but for the sake of brevity, here’s a quick list:
Obviously, there are many more topics of research, but the one thing they all have in common is the proliferation of big data and how useful it can be to conclusive outcomes.
Another area in which big data and analytics can be used to improve the healthcare experience for patients in staff is in the day-to-day running of medical centres. Big data can be used to identify strengths and weaknesses as well as make predictions and identify trends in hospital management.
Again, here are just a few situations in which this could be useful…
An Electronic Health Record (EHR) system is the very definition of big data. It involves accumulating patient data and storing it to be shared across a network of healthcare providers. Data includes medical history, medication, allergies, contact with healthcare specialists and anything else that is pertinent to the patient.
Storing and sharing data in this manner means that patients can have consistent care no matter which part of the healthcare system they are in contact with. GPs share the information with hospitals who share it with specialists such as physiotherapists, psychologists or surgeons.
Another benefit of this system is that it can provide a snapshot of the evolution of a patient’s health. A quick analysis of this data will reveal trends and show if any medical condition is worsening or improving and guide any intervention that may be needed. This can prevent hospitalisation, reducing the burden on the healthcare system, and improve the patient’s quality of life.
One of the big challenges facing hospitals in the modern world is understanding how much staff are needed to ensure the smooth running of hospital services. Too few and waiting times for emergency or critical care situations may rise, lowering the quality of care for the patient. Too many and the hospital, which more than likely already has strict budgetary requirements, can haemorrhage money.
By analysing big data, hospital directors and human resource managers can identify trends in patient numbers based on a variety of metrics and, subsequently, make informed decisions on staffing levels. Metrics might include details such as time of day, week or year, seasonality, predicted epidemics such as flu, pandemics such as COVID, and any other number of criteria.
Check out this white paper on predicted ER visits and admissions in French hospitals by Intel to get a better understanding of how this might work.
In a hospital setting, identifying training and development needs can mean the difference between life and death. But there is a massive disparity in the skills that different staff members need to do their jobs well. Someone working in admin, for example, will have wildly different training needs to a surgeon.
So how can hospital managers keep on top of this? Well, surprise surprise, big data can help in this regard. Collecting, storing and analysing staff performance at all levels can, again, reveal trends and identify areas of weakness which can be bolstered with appropriate training.
In addition, analytical tools such as Codimg are extremely useful as debriefing tools after real or simulated incidents. They can provide objective data on staff performance, identify mistakes and ensure that staff performance is in line with hospital standards.
A lot changed during the COVID pandemic. From the way we do business to our social lives, new rules, regulations and precautions turned the world on its head, making isolation and quarantine the norm for many.
And healthcare provision also changed in this time with the rise of telehealth and telemedicine.
Although not new concepts, distance healthcare allowed people in lockdown with non-critical illnesses to keep in contact with doctors and nurses and allowed for the remote administration of care. Areas of telehealth include:
To get an overview of these concepts, we suggest you read our article on Telehealth and Telemedicine.
Obviously, with new ways of working come new challenges, and an ever increasing need to monitor the effectiveness of the services being provided. Again, the analysis of big data can be helpful in this regard.
In the case of telemedicine, the majority of the collected data will be in the form of video, recorded from remote calls to patients. Codimg is particularly useful for analysing this type of data, as we’ll explain in the next part of this article.
In this section, we’ll focus on some tools that can be used for the collection and analysis of big data. Obviously, we’ll focus on our own software (our blog, our rules!), but we’ll also give you an overview of some other tools which can be used in conjunction with Codimg to get a more complete analysis solution.
So, without further ado, let’s get started…
In general, Codimg is used as a tool for the analysis of “observed” actions. That is, data can be collected either in real-time or through pre-recorded video. This is supremely useful for many of the applications that we’ve mentioned above. Medical simulation, clinical trials, behavioural studies, training needs analysis and many other facets of healthcare management can be analysed with Codimg.
So, how does it work?
Ok, as a quick overview, the user creates an analysis template or digital checklist which contains all the metrics they want to observe. Metrics will include main actions and “descriptors” which describe the main action and how it is carried out. While either observing live or through video, and using this template, the user “tags” events that happen during a simulation, interview, or study. This creates a short video clip with associated data, both of which are added to a database which can then be reviewed and analysed using the analytical tools available in Codimg.
The following are examples of the type of analysis templates or digital checklists that can be created using Codimg, which we’d be happy to send to anyone who currently uses Codimg, or anyone who downloads a free trial of our software. Contact us for more information.
Digital Checklist for Child Welfare in Forensic Psychology Interviews
Template for the Analysis of Cohort Interviews during Clinical Trials
Digital Checklist for the Rights of Medication Administration
It’s important to remember that these are only examples and analysis templates made with Codimg are fully customisable to your needs.
As we said, this is just a quick overview on how Codimg can be used for data collection. If you’d like to know more about the analysis tools available in Codimg, we’d invite you to browse our website, which will give you much more info. You can also contact us to discuss your needs and we’d be happy to provide you with a demonstration of what the software can do.
Data visualisation is one of the most important aspects of big data analysis. Visualisation takes the raw collected data and turns it into information that is easy for the average human being to understand.
Visualisation can take the form of bar graphs, pie charts, data labels, heat maps, fever charts and many other eye-catching graphics.
Data collected with Codimg can be exported in a variety of formats, including Excel spreadsheets, which can then be used in external programs which specialise in data visualisation.
So, let’s take a look at some data visualisation tools, starting off with our own…
While perhaps not as powerful as some of the other tools that appear in this list, Codimg, nevertheless, comes packaged with its own data visualisation tool. Using the Codimg dashboard, creating charts, graphs and labels is a quick and easy process.
The big advantage in using Codimg in this manner is, of course, that you don’t have to leave the program to create your data visualisation. The data is collected and stored using Codimg and it’s automatically connected to your charts and graphs. This means that dashboards are also interactive in so far as clicking on any part of a chart or graphic will open the relevant video clips.
Check this video for more info on Codimg dashboards:
Power BI is one of the most well-known data visualisation and business intelligence suites on the market. Developed by Microsoft, this powerful software allows the user to feed in data from a variety of sources in order to create coherent, interactive visual dashboards.
Here’s an example of how it can be used to create a dashboard specifically for the healthcare industry:
Tableau is another big player in the field of data visualisation. Developed by researchers at Stanford University, Tableau functions in a very similar way to the above mentioned Power BI, although there are some major differences in the type of data they can handle and the speed they process that data. This article gives a good overview of the differences between Tableau and Power BI.
The following video shows another healthcare dashboard created using Tableau.
Although Power BI and Tableau are probably the two biggest data visualisation programs on the market, there are a few other notable examples. You might want to also consider one of the following:
Data warehousing is a relatively new concept in terms of big data. Basically speaking, a data warehouse allows businesses to store and process the huge amounts of data that can be generated over the course of time.
Data can come from a wide variety of sources and the warehouse application sorts and stores this information in a highly organised manner which provides easy access for analysts.
This is a fairly advanced concept, so we won’t go into great detail in this article about the process. Instead, we’ll suggest a few data warehousing services which you can peruse at your leisure:
Although not a data warehouse per se, at this point, we’d like to mention our own solution for big data storage - Sharimg.com.
Sharimg is an online platform specifically designed for storage of data and analysis generated by Codimg. By using this service, you can upload video enabled databases to the cloud and share them with your team.
Sharimg is fully integrated into Codimg and data can be uploaded directly from our software. To find out more about this great storage solution, have a read of this article and, to get a free trial of Sharimg, get in contact with us.
Since its conception, big data has transformed business intelligence, providing insights and revealing trends to organisations and institutions around the world.
And the same is true for healthcare. Big data has the ability to transform operational and analytical procedures in hospitals and medical centres, improving patient care and safety in the process.
And Codimg is at the cutting edge of data collection and analysis. A fully customisable solution which provides quick, objective analysis and is compatible with some of the world’s best known business tools, you can try it free today.
To set up a trial period and to discuss your needs, please feel free to get in contact with us at any time.
If you have any questions about anything that was raised in this article, we’d be happy to answer them. Get in touch through the link above or reach out on any of our social networking channels. We’re here to help.
Until then, thanks for reading.
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