Infographics

This is a list of visualisations (infographics) found in this blog that are beautiful and engaging, easier to understand than words alone.

You can view a larger image of any of the following infographics posted in this blog by first clicking on any of the links list below, which will open a new tab in your current window to display a post from this blog showing the relevant infographic image. Second, click the infographic image from the post in the new tab of your current window. You will then see the larger image of the infographic in another new tab in your current window.

 

Electronic vs Paper medical records
The infographic in this post is a typical scenario of “missing” medical records, and offsite storage which continues to post many problems from logistics to damaged medical records.

ICD 11 – history of the development of the ICD from 1853 to 2015
A showcase of all the past revisions of ICD leading to ICD 11 expected in 2015.

ICD 10 & ICD 11 Development – How, What, Why & When
An infographic that summarises facts on ICD 10 & ICD 11 Development – How, What, Why & When, which are by no means exhaustive.

What is Big Data?
Key facts about Big Data
.

Format Of ICD-10 Diagnosis Code
A self-explanatory ICD 10 code infographic example.

Top 20 Most Popular EMR Software Solutions
A good visual for about EMRs solutions out there.

What is the Cloud?
The Cloud is the Internet.

Technology vs Paper – A Recent History of Medical Records
Most of us accept that medical records are still kept in paper files, and that’s the way it is.

EHR vs. Traditional Paper Records
Differences between EHR(EMR) and traditional paper-based records.

Diabetes Control Chart
To tell you what Glycated/Glycosylated Hemoglobin (HbA1c or A1c test) is all about.

Recent Posts

Voice-to-text medical software using NLP technology

When the doctor sits down with you on your visit, the doctor normally spends a lot of time inputting the how and the why of what’s happening to you, conventionally into a paper-based case note/medical record.

These free text narratives are further aggravated as not all doctors “speak the same way” in note creation and management.

These notes about your condition are rendered not easily extractable in ways that the data can be analyzed by a computer.

The good thing is this unstructured data of free text has given way to more and more ways to digital record-keeping—into the electronic health record systems (EHRs) way, away from the days of trying to decipher doctors’ medical lingo on hand written medical records and medical reports. However, EHRs are as unstructured patient data like its cousin, the paper-based medical record.

Inevitably, EHRs create challenges for doctors and that can be frustrating with additional data input responsibilities often bogged down by form-filling through the many clicks and screens required to navigate their EHRs, as well as they spending additional hours on updating EHRs.

EHRs became more important to be accurate and immediate with the scourge of the COVID-19 pandemic and with an increased reliance on contact-free consultations between doctors and patients.

Ultimately, huge volumes of unstructured patient data continue to be input into EHRs on a daily basis. As healthcare documentation is mostly unstructured, and it therefore goes largely unutilised, since mining and extraction of this data is challenging and resource intensive.

Medical Natural Language Processing (NLP) is steadily proving to be a solution to this challenge, creating new and exciting opportunities for healthcare delivery and patient experience. The adoption of NLP in healthcare is rising because of its recognized potential to search, analyze and interpret mammoth amounts of patient datasets.

Human beings use text and spoken words to fill up the human language with homonyms, homophones, sarcasm, idioms, metaphors, grammar and usage exceptions, variations in sentence structure, as some examples of ambiguities and irregularities as only they understand their usage.

NLP is a branch of artificial intelligence (AI) concerned with giving computers the ability to understand text and spoken words in much the same way we human beings can.

It is the main concept behind translation and personal assistance apps like Google Translate, OK Google, Siri, Cortana, and Alexa.

Without NLP technology using NLP healthcare tools capable of scrubbing large sets of unstructured health data, that data is not in a usable format for modern computer-based algorithms to easily access, extract, and accurately interpret clinical documentation of the actual patient record previously considered buried in text form.

NLP technology services accurately give voice to the unstructured data of the healthcare universe while processing the content of long chart notes of medical records, giving incredible insight into understanding quality, improving methods, and better results for patients that helps determine the disease burden and valuable decision support can be obtained.

Augnito is a voice-to-text medical software using NLP technology hoping  to improve healthcare, but for now specifically developed for the Indian market launched six months ago, and now being used in 24 States in India.

The voice has become the most powerful tool in technology today. Just by talking, the voice is the most natural way of communication for humans. We are able to do sophisticated and important jobs with gadgets like Alexa.

Like the Alexa gadget been able to do sophisticated and important jobs using voice controlled NLP technology, the Augnito software available for a monthly subscription on both Mac and Windows platforms, types out notes that are dictated to and saves it in an editable textual format on a cloud server.

The Augnito voice recognition software has a pre-programmed list of medical terms (its vocabulary database is constantly updated in keeping with doctors’ requirements and feedback), a built-in editor, report templates and keyboard shortcuts that help reduce repetitive typing.

Voice recognition software like Augnito using NLP technology, has the potential to boost a doctor’s productivity at a time of increased online consultations.

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