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.

Global COVID-19 Clinical Characterization Case Record Form

In the wake of COVID-19, I have been thinking how coronavirus data is been captured into a typical medical record. A check around the Internet led me to the World Health Organisation [WHO] recommended rapid clinical characterisation case record form (clinical CRF).

Like the one standardised form i.e. The World Health Organisation (WHO) International Form of Medical Certificate of Cause of Death to collect mortality data among member states—with the clinicial CRF form also by the WHO, the WHO intends that by using one standardised clinical data tool, there is potential for clinical data from around the world to be aggregated; in order to learn more to inform the public health response and prepare for large scale clinical trials.

This form is intended to provide member states with a standardised approach to collect clinical data in order to better understand the natural history of this disease and describe clinical phenotypes and treatment interventions (i.e. clinical characterisation) for Covid-19.

Some important stuff to take note if implementing this form include:

1: this CRF has 3 (M)odules to be completed—(M1)for first day of admission to the health centre, (M2) on first day of admission to ICU or high dependency unit, also be completed daily for as many days as resources allow and continued to follow-up patients who transfer between wards, and (M3) to be completed at discharge or death; and,

2: Internet services are required to enter data to the central electronic REDCap database or to your site/network’s independent database; the form guidelines suggest that printed paper CRFs may be used and the data can be typed into the electronic database afterwards.

The form can be viewed from the link (the link will open in a new tab of your current window) in the reference given below.

Reference:
Coronavirus disease (COVID-19) technical guidance: Patient management, Case Management, WHO, <https://www.who.int/docs/default-source/coronaviruse/who-ncov-crf.pdf?sfvrsn=84766e69_4>

ICD Coding advice from the WHO for the 2019 novel coronavirus (COVID-19)

The purpose of this post is to share the World Health Organization [WHO] official diagnosis coding guidance update for health care encounters and deaths related to the 2019 novel coronavirus (COVID-19) previously named 2019-nCoV.

Screenshot image from ICD-10

ICD-10 Chapter XXII: Codes for special purposes has a special sub-category called U07 Emergency use of U07. WHO’s recent creation of an emergency ICD-10 code U07.1, is assigned to the disease diagnosis of 2019-nCoV acute respiratory disease. See my screenshot image from ICD-10 to the left.

 

The title of U07 will be changed back to ‘codes for emergency use’.

It is to be noted that the name ‘2019-nCoV’ is temporary and is likely to change (to be independent of date and virus family, and for consistency with international virus taxonomy).

For ICD-11, the code for the illness would be RA01.0

A new and final ICD-10-CM code title for the COVID-19 diagnosis code is now [March 17–18, 2020] been established, and will be implemented effective October 1, 2020, by the Centers for Disease Control and Prevention’s (CDC), USA and the National Center for Health Statistics (NCHS), USA.

References:

  1. Emergency use ICD codes for COVID-19 disease outbreak, Classifications, WHO, <http://www9.who.int/classifications/icd/covid19/en/>
  2. Organizations Developing New Codes for COVID-19, and a Primer on the Virus, Under the Dome, Journal of AHIMA, <https://journal.ahima.org/new-icd-10-cm-code-for-covid-19-becomes-effective-october-1/>

ICD-11 2018 version: Part 2 – The ICD-11 Menu Hierarchy

The ICD-11 Homepage at https://icd.who.int/ (opens in a new tab of this same open window) of the World Health Organisation (WHO) website is a specific point in time when you “Discover” the ICD-11 interface to then “Explore” to both the whole time you are looking for something about ICD-11 and the time you discovered something about ICD-11.

From this Homepage, the reader discovers a top-level menu that brings you to various “places” within the ICD-11 Homepage. Below is a chart (you can view a larger image of this chart by clicking on this chart which will open in a new tab of this same open window) from my discovery of these various “places” and what I found from my exploration.  This chart shows the ICD-11 Menu Hierarchy as I had discovered and explored. In future posts, I shall write more on each individual menu item.