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.

MRPM.VOW.02.14: Healthcare’s four-letter word? It’s ‘silo


To continue the series of videos for 2014, this week’s Video of the Week (VOW) pick is about the change that must evolve when it is common practice that hospitals, clinics and doctor’s offices, digital health devices and services all continue to keep close tabs on their information and silo their data.

Those of you who have been following the trends at healthcare conferences and exhibitions for some time will recognise it is common at one after another conference and exhibition when we see vendors show off systems that work great, but we soon realise that they don’t get along with each other. I think it has become less of a desire to acquire new systems and more of a requirement in the rapidly changing healthcare industry, keeping up with new technologies and innovations.

You would also already been aware that departments within the hospital or health network, digital health devices and services all keep close tabs on their information and have control over a certain subset of data that they’re not into sharing, they just all seem to silo their data.

And for those of you who are familiar with mHealth, surely are all too familiar that mHealth devices continue to over-emphasize the collection of vital signs and real-time transmission to healthcare providers.

Enters Patrick Soon-Shiong, a South African-born surgeon who is pushing for a vision of integrated healthcare through a network of digital, genomic and clinical solutions. He envisions a future healthcare system as an integrated system that connects all the dots which follows a human being through the continuum of life serving a patient throughout his or her life, not just in sickness.

In the accompanying video (click on http://bcove.me/2cwzbmrg to watch the video, which will open in a new tab of your current browser window), watch and listen to Soon-Shiong discoursing among other things the following views:

  • healthcare has to break the rule of capturing vital signs at all times and focus more on gathering data and identifying trends;
  • likens a health journey much like a long plane trip, during which a true operating system which encompasses clinical decision support, machine learning and “adaptive amplified intelligence” pulls in data from all sources that “integrates pieces of the puzzle” and gives you inputs from the consumer so that the caretaker can plot a course, and adjust that course as things happen and manage outputs;
  • that the Electronic Medical Record (EMR) is “basically a flight log” that needs to be tapped for information at times which could be a part of that solution, but not the whole solution;
  • he believes that healthcare isn’t being held back by technology as a barrier, rather the real problem is a workflow management problem since technology is not been used properly, and is falling behind other industries like banking and entertainment;
  • nobody is taking the trouble of taking each of their siloed pieces and integrating them into a single healthcare system; and
  • he concludes that change management as the next challenge while taking advantage of the fear to resist wholesale change in healthcare.


  1. Healthcare’s four-letter word? It’s ‘silo’, mHealth News, viewed 28 April 2014, <http://www.mhealthnews.com/news/healthcares-four-letter-word-its-silo?single-page=true>

  2. Healthcare’s four-letter word? It’s ‘silo’, mHealth News, viewed 28 April 2014, <http://bcove.me/2cwzbmrg>

Five Reasons Why Electronic Medical Records Are Good For Patients

Investment in developing a good Electronic Medical Record (EMR) system to provide value to patients by driving up safety, quality, operational excellence, transparency and access can be seen as shown by the example at Cleveland Clinic Abu Dhabi, a carefully designed EMR system modelled after the famous EMR model at Cleveland Clinic, Ohio, United States – a long time leader in EMR systems.

The infographic below (click on the image to open in a new tab of your current window to view a larger image) shows a summary of five (5) good reasons why EMRs are good for patients as from the example at Cleveland Clinic Abu Dhabi.



  1. Five Reasons Why Electronic Medical Records Are Good For Patients, Marc, H 2013, LinkedIn, viewed 15 July 2013, <http://www.linkedin.com/influencers/20130715101824-13527628-five-reasons-why-electronic-medical-records-are-good-for-patients>

Healthcare Big Data – Part 2a

Big Data 3Vs cardboard-box-iconIn the post Healthcare Big Data – Part 2 (this link will open in a new tab of your current browser window), I wrote that no matter the size of Healthcare Big Data, a known fact of the current state of healthcare industry worldwide which is in general afflicted with poorly coordinated care, fraud and abuse and administrative and clinical efficiency, the goal is ultimately to improve patient care and reduce costs.

In this post I like to share with you this infographic below (click on the image of the infographic below to view a larger image which will first open  in a new tab of your current browser window, click again on the image in this new tab which will then show you a full view of the infographic in the same tab) which I think rightly supplements what I wrote in the post mentioned above.

This infographic visualises the worldwide trend to digitize healthcare patient information from paper-based medical records to Electronic Medical Records. This trend continues to gather increasing momentum to produce infinite volumes of Big Data, an estimated 50 pentabytes of data in the healthcare realm. This influx of Big Data will create more jobs to handle all these data, especially new jobs that demand new talent in analytics,

This infographic also visualises the bulk of the internal source of Healthcare Big Data as originated by medical providers and ancillary services providers during the course of providing their services. More Big Data is accumulated when these internal data source is in turn used for insurance claims and payments, to a greater extent In advanced economies and lesser in less advanced economies. The technology vendors provide the technology interface for the internal source of Healthcare Big Data.

Then there is the external and public as well as private storage of Healthcare Big Data. Public Health agencies also generate Healthcare Big Data mandated by legislation and regulations e.g. immunisation and cancers data, and store them in data repositories. Third-party organisations also generate Healthcare Big Data when they coordinate between healthcare providers. Private data are also stored in remotely stored and web-based repositories when some consumers maintain personal (private) health records online.

From this infographic, patient care is improved when streaming data is used to decrease patient mortality as these data moves in healthcare. However the bigger challenge is to harness the 80% of all the unstructured data of patient information in Healthcare Big Data.

When it comes to healthcare Big Data is a Big Deal

Infographic credit: healthcareitconnect.com/

I shall discuss the ways of Big Data which will transform healthcare, in the near future with cost savings, quality of care, and care coordination after I have blogged about Big Data solutions in a future post.

Healthcare Big Data – Part 2

Big Data 3Vs cardboard-box-iconIn this second instalment of Healthcare Big Data, let’s look at the multiple sources of data that are responsible for Healthcare Big Data.

The internal data found in existing paper-based medical records is one large source of Healthcare Big Data.

With more and more hospitals in the health care industry around the world turning to creating digital representations of existing data in paper-based medical records and acquiring everything that is new in the form of Electronic Medical Records, there is an infinite data growth rate in this internal data source.

Then there is also Big Data from other sources, those from external, private, and public sources.

The discovery process, both oral and written discovery initiated by the legal profession outside the healthcare industry which adds terabytes or even petabytes of information is one source of external Healthcare Big Data, when individual doctors, hospitals, and medical practice groups become defendants in malpractice lawsuits.

No matter the size of Healthcare Big Data, a known fact in healthcare is to improve patient care and reduce costs.

Thus to improve patient care and reduce costs through Healthcare Big Data, one of the biggest challenges for most healthcare organisations is to mine the data or dig for something of value from these multiple sources of data. Healthcare organisations must find i.e locate the appropriate data, identify useful data i.e determine whether the data set is appropriate for use,  and aggregate all of the Big Data from the multiple sources and push through an analytics platform as part of their analytics processes.

Since I am running a blog for the general benefit of Health Information Management (HIM) / Medical Records (MR) practitioners, I shall not be diving deeper into big data sources, to avoid driving readers into the IT realm nor writing on the business analytics (BA) and business intelligence (BI) processes to determine how large-scale data sets can be used. I must say that all the posts on Big Data I have published on this website-blog , including this one is to facilitate HIM / MR practitioners to have a rudimentary understanding of Big Data.

Now that HIM / MR practitioner readers  know that Big Data is out there, Frank (2013) states that “analytics is part science, part investigative work, and part assumption.” The idea is to capture as much as data the healthcare organisation deals with, so all of any data are located, included and gathered from as many data sources as possible so that the more data there will be to work with and bring all of these data into an analytics platform.

While the healthcare organisation locates, includes and gathers from as many data sources, healthcare organisations will find a vast wealth of external public information. This external data makes up the public portion of Big Data. This includes customer sentiments from research companies and social networking sites e.g Twitter, Facebook to geopolitical issues e.g. weather information and traffic pattern information, from government entities, e.g census data, and a multitude of other sources.

In the next instalment, I shall gather more information on how the multitude of sources of Healthcare Big Data must be integrated and managed to set priorities so that Big Data solutions could analyse and get the results into the right hands to improve patient care and reduce costs.


  1. Frank JO 2013, Big Data Analytics: Turning Big Data into Big Money, Wiley and SAS Business Series, John Wiley & Sons, Inc, New Jersey, USA